Microsoft.MachineLearningServices workspaces/schedules 2024-07-01-preview

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• 2024-10-01
• 2024-10-01-preview
• 2024-07-01-preview
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• 2022-06-01-preview

Bicep resource definition

The workspaces/schedules resource type can be deployed with operations that target:

• Resource groups - See resource group deployment commands

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/schedules resource, add the following Bicep to your template.

resource symbolicname 'Microsoft.MachineLearningServices/workspaces/schedules@2024-07-01-preview' = {
  name: 'string'
  properties: {
    action: {
      actionType: 'string'
      // For remaining properties, see ScheduleActionBase objects
    }
    description: 'string'
    displayName: 'string'
    isEnabled: bool
    properties: {
      {customized property}: 'string'
    }
    tags: {
      {customized property}: 'string'
    }
    trigger: {
      endTime: 'string'
      startTime: 'string'
      timeZone: 'string'
      triggerType: 'string'
      // For remaining properties, see TriggerBase objects
    }
  }
}

TriggerBase objects

Set the triggerType property to specify the type of object.

For Cron, use:

{
  expression: 'string'
  triggerType: 'Cron'
}

For Recurrence, use:

{
  frequency: 'string'
  interval: int
  schedule: {
    hours: [
      int
    ]
    minutes: [
      int
    ]
    monthDays: [
      int
    ]
    weekDays: [
      'string'
    ]
  }
  triggerType: 'Recurrence'
}

JobInput objects

Set the jobInputType property to specify the type of object.

For custom_model, use:

{
  jobInputType: 'custom_model'
  mode: 'string'
  uri: 'string'
}

For literal, use:

{
  jobInputType: 'literal'
  value: 'string'
}

For mlflow_model, use:

{
  jobInputType: 'mlflow_model'
  mode: 'string'
  uri: 'string'
}

For mltable, use:

{
  jobInputType: 'mltable'
  mode: 'string'
  uri: 'string'
}

For triton_model, use:

{
  jobInputType: 'triton_model'
  mode: 'string'
  uri: 'string'
}

For uri_file, use:

{
  jobInputType: 'uri_file'
  mode: 'string'
  uri: 'string'
}

For uri_folder, use:

{
  jobInputType: 'uri_folder'
  mode: 'string'
  uri: 'string'
}

ScheduleActionBase objects

Set the actionType property to specify the type of object.

For CreateJob, use:

{
  actionType: 'CreateJob'
  jobDefinition: {
    componentId: 'string'
    computeId: 'string'
    description: 'string'
    displayName: 'string'
    experimentName: 'string'
    identity: {
      identityType: 'string'
      // For remaining properties, see IdentityConfiguration objects
    }
    isArchived: bool
    notificationSetting: {
      emailOn: [
        'string'
      ]
      emails: [
        'string'
      ]
      webhooks: {
        {customized property}: {
          eventType: 'string'
          webhookType: 'string'
          // For remaining properties, see Webhook objects
        }
      }
    }
    properties: {
      {customized property}: 'string'
    }
    services: {
      {customized property}: {
        endpoint: 'string'
        jobServiceType: 'string'
        nodes: {
          nodesValueType: 'string'
          // For remaining properties, see Nodes objects
        }
        port: int
        properties: {
          {customized property}: 'string'
        }
      }
    }
    tags: {
      {customized property}: 'string'
    }
    jobType: 'string'
    // For remaining properties, see JobBaseProperties objects
  }
}

For CreateMonitor, use:

{
  actionType: 'CreateMonitor'
  monitorDefinition: {
    alertNotificationSettings: {
      emailNotificationSettings: {
        emails: [
          'string'
        ]
      }
    }
    computeConfiguration: {
      computeType: 'string'
      // For remaining properties, see MonitorComputeConfigurationBase objects
    }
    monitoringTarget: {
      deploymentId: 'string'
      modelId: 'string'
      taskType: 'string'
    }
    signals: {
      {customized property}: {
        notificationTypes: [
          'string'
        ]
        properties: {
          {customized property}: 'string'
        }
        signalType: 'string'
        // For remaining properties, see MonitoringSignalBase objects
      }
    }
  }
}

For InvokeBatchEndpoint, use:

{
  actionType: 'InvokeBatchEndpoint'
  endpointInvocationDefinition: any(Azure.Bicep.Types.Concrete.AnyType)
}

Nodes objects

Set the nodesValueType property to specify the type of object.

For All, use:

{
  nodesValueType: 'All'
}

DistributionConfiguration objects

Set the distributionType property to specify the type of object.

For Mpi, use:

{
  distributionType: 'Mpi'
  processCountPerInstance: int
}

For PyTorch, use:

{
  distributionType: 'PyTorch'
  processCountPerInstance: int
}

For TensorFlow, use:

{
  distributionType: 'TensorFlow'
  parameterServerCount: int
  workerCount: int
}

Webhook objects

Set the webhookType property to specify the type of object.

For AzureDevOps, use:

{
  webhookType: 'AzureDevOps'
}

TargetLags objects

Set the mode property to specify the type of object.

For Auto, use:

{
  mode: 'Auto'
}

For Custom, use:

{
  mode: 'Custom'
  values: [
    int
  ]
}

PredictionDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

{
  dataType: 'Categorical'
  metric: 'string'
}

For Numerical, use:

{
  dataType: 'Numerical'
  metric: 'string'
}

DataDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

{
  dataType: 'Categorical'
  metric: 'string'
}

For Numerical, use:

{
  dataType: 'Numerical'
  metric: 'string'
}

MonitorComputeIdentityBase objects

Set the computeIdentityType property to specify the type of object.

For AmlToken, use:

{
  computeIdentityType: 'AmlToken'
}

For ManagedIdentity, use:

{
  computeIdentityType: 'ManagedIdentity'
  identity: {
    type: 'string'
    userAssignedIdentities: {
      {customized property}: {}
    }
  }
}

SparkJobEntry objects

Set the sparkJobEntryType property to specify the type of object.

For SparkJobPythonEntry, use:

{
  file: 'string'
  sparkJobEntryType: 'SparkJobPythonEntry'
}

For SparkJobScalaEntry, use:

{
  className: 'string'
  sparkJobEntryType: 'SparkJobScalaEntry'
}

JobBaseProperties objects

Set the jobType property to specify the type of object.

For AutoML, use:

{
  environmentId: 'string'
  environmentVariables: {
    {customized property}: 'string'
  }
  jobType: 'AutoML'
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings: {
    jobTier: 'string'
  }
  resources: {
    dockerArgs: 'string'
    instanceCount: int
    instanceType: 'string'
    properties: {
      {customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
    }
    shmSize: 'string'
  }
  taskDetails: {
    logVerbosity: 'string'
    targetColumnName: 'string'
    trainingData: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    taskType: 'string'
    // For remaining properties, see AutoMLVertical objects
  }
}

For Command, use:

{
  codeId: 'string'
  command: 'string'
  distribution: {
    distributionType: 'string'
    // For remaining properties, see DistributionConfiguration objects
  }
  environmentId: 'string'
  environmentVariables: {
    {customized property}: 'string'
  }
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  jobType: 'Command'
  limits: {
    jobLimitsType: 'string'
    timeout: 'string'
  }
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings: {
    jobTier: 'string'
  }
  resources: {
    dockerArgs: 'string'
    instanceCount: int
    instanceType: 'string'
    properties: {
      {customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
    }
    shmSize: 'string'
  }
}

For FineTuning, use:

{
  fineTuningDetails: {
    model: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    taskType: 'string'
    trainingData: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
    validationData: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
    modelProvider: 'string'
    // For remaining properties, see FineTuningVertical objects
  }
  jobType: 'FineTuning'
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings: {
    jobTier: 'string'
  }
  resources: {
    instanceTypes: [
      'string'
    ]
  }
}

For Pipeline, use:

{
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  jobs: {
    {customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
  }
  jobType: 'Pipeline'
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  settings: any(Azure.Bicep.Types.Concrete.AnyType)
  sourceJobId: 'string'
}

For Spark, use:

{
  archives: [
    'string'
  ]
  args: 'string'
  codeId: 'string'
  conf: {
    {customized property}: 'string'
  }
  entry: {
    sparkJobEntryType: 'string'
    // For remaining properties, see SparkJobEntry objects
  }
  environmentId: 'string'
  environmentVariables: {
    {customized property}: 'string'
  }
  files: [
    'string'
  ]
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  jars: [
    'string'
  ]
  jobType: 'Spark'
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  pyFiles: [
    'string'
  ]
  queueSettings: {
    jobTier: 'string'
  }
  resources: {
    instanceType: 'string'
    runtimeVersion: 'string'
  }
}

For Sweep, use:

{
  earlyTermination: {
    delayEvaluation: int
    evaluationInterval: int
    policyType: 'string'
    // For remaining properties, see EarlyTerminationPolicy objects
  }
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  jobType: 'Sweep'
  limits: {
    jobLimitsType: 'string'
    maxConcurrentTrials: int
    maxTotalTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  objective: {
    goal: 'string'
    primaryMetric: 'string'
  }
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings: {
    jobTier: 'string'
  }
  samplingAlgorithm: {
    samplingAlgorithmType: 'string'
    // For remaining properties, see SamplingAlgorithm objects
  }
  searchSpace: any(Azure.Bicep.Types.Concrete.AnyType)
  trial: {
    codeId: 'string'
    command: 'string'
    distribution: {
      distributionType: 'string'
      // For remaining properties, see DistributionConfiguration objects
    }
    environmentId: 'string'
    environmentVariables: {
      {customized property}: 'string'
    }
    resources: {
      dockerArgs: 'string'
      instanceCount: int
      instanceType: 'string'
      properties: {
        {customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
      }
      shmSize: 'string'
    }
  }
}

AutoMLVertical objects

Set the taskType property to specify the type of object.

For Classification, use:

{
  cvSplitColumnNames: [
    'string'
  ]
  featurizationSettings: {
    blockedTransformers: [
      'string'
    ]
    columnNameAndTypes: {
      {customized property}: 'string'
    }
    datasetLanguage: 'string'
    enableDnnFeaturization: bool
    mode: 'string'
    transformerParams: {
      {customized property}: [
        {
          fields: [
            'string'
          ]
          parameters: any(Azure.Bicep.Types.Concrete.AnyType)
        }
      ]
    }
  }
  limitSettings: {
    enableEarlyTermination: bool
    exitScore: int
    maxConcurrentTrials: int
    maxCoresPerTrial: int
    maxTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  nCrossValidations: {
    mode: 'string'
    // For remaining properties, see NCrossValidations objects
  }
  positiveLabel: 'string'
  primaryMetric: 'string'
  taskType: 'Classification'
  testData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  testDataSize: int
  trainingSettings: {
    allowedTrainingAlgorithms: [
      'string'
    ]
    blockedTrainingAlgorithms: [
      'string'
    ]
    enableDnnTraining: bool
    enableModelExplainability: bool
    enableOnnxCompatibleModels: bool
    enableStackEnsemble: bool
    enableVoteEnsemble: bool
    ensembleModelDownloadTimeout: 'string'
    stackEnsembleSettings: {
      stackMetaLearnerKWargs: any(Azure.Bicep.Types.Concrete.AnyType)
      stackMetaLearnerTrainPercentage: int
      stackMetaLearnerType: 'string'
    }
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
  weightColumnName: 'string'
}

For Forecasting, use:

{
  cvSplitColumnNames: [
    'string'
  ]
  featurizationSettings: {
    blockedTransformers: [
      'string'
    ]
    columnNameAndTypes: {
      {customized property}: 'string'
    }
    datasetLanguage: 'string'
    enableDnnFeaturization: bool
    mode: 'string'
    transformerParams: {
      {customized property}: [
        {
          fields: [
            'string'
          ]
          parameters: any(Azure.Bicep.Types.Concrete.AnyType)
        }
      ]
    }
  }
  forecastingSettings: {
    countryOrRegionForHolidays: 'string'
    cvStepSize: int
    featureLags: 'string'
    forecastHorizon: {
      mode: 'string'
      // For remaining properties, see ForecastHorizon objects
    }
    frequency: 'string'
    seasonality: {
      mode: 'string'
      // For remaining properties, see Seasonality objects
    }
    shortSeriesHandlingConfig: 'string'
    targetAggregateFunction: 'string'
    targetLags: {
      mode: 'string'
      // For remaining properties, see TargetLags objects
    }
    targetRollingWindowSize: {
      mode: 'string'
      // For remaining properties, see TargetRollingWindowSize objects
    }
    timeColumnName: 'string'
    timeSeriesIdColumnNames: [
      'string'
    ]
    useStl: 'string'
  }
  limitSettings: {
    enableEarlyTermination: bool
    exitScore: int
    maxConcurrentTrials: int
    maxCoresPerTrial: int
    maxTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  nCrossValidations: {
    mode: 'string'
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric: 'string'
  taskType: 'Forecasting'
  testData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  testDataSize: int
  trainingSettings: {
    allowedTrainingAlgorithms: [
      'string'
    ]
    blockedTrainingAlgorithms: [
      'string'
    ]
    enableDnnTraining: bool
    enableModelExplainability: bool
    enableOnnxCompatibleModels: bool
    enableStackEnsemble: bool
    enableVoteEnsemble: bool
    ensembleModelDownloadTimeout: 'string'
    stackEnsembleSettings: {
      stackMetaLearnerKWargs: any(Azure.Bicep.Types.Concrete.AnyType)
      stackMetaLearnerTrainPercentage: int
      stackMetaLearnerType: 'string'
    }
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
  weightColumnName: 'string'
}

For ImageClassification, use:

{
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    momentum: int
    nesterov: bool
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    trainingBatchSize: int
    trainingCropSize: int
    validationBatchSize: int
    validationCropSize: int
    validationResizeSize: int
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
    weightedLoss: int
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      momentum: 'string'
      nesterov: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      trainingBatchSize: 'string'
      trainingCropSize: 'string'
      validationBatchSize: 'string'
      validationCropSize: 'string'
      validationResizeSize: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
      weightedLoss: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  taskType: 'ImageClassification'
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
}

For ImageClassificationMultilabel, use:

{
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    momentum: int
    nesterov: bool
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    trainingBatchSize: int
    trainingCropSize: int
    validationBatchSize: int
    validationCropSize: int
    validationResizeSize: int
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
    weightedLoss: int
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      momentum: 'string'
      nesterov: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      trainingBatchSize: 'string'
      trainingCropSize: 'string'
      validationBatchSize: 'string'
      validationCropSize: 'string'
      validationResizeSize: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
      weightedLoss: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  taskType: 'ImageClassificationMultilabel'
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
}

For ImageInstanceSegmentation, use:

{
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    boxDetectionsPerImage: int
    boxScoreThreshold: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    imageSize: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    maxSize: int
    minSize: int
    modelName: 'string'
    modelSize: 'string'
    momentum: int
    multiScale: bool
    nesterov: bool
    nmsIouThreshold: int
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    tileGridSize: 'string'
    tileOverlapRatio: int
    tilePredictionsNmsThreshold: int
    trainingBatchSize: int
    validationBatchSize: int
    validationIouThreshold: int
    validationMetricType: 'string'
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      boxDetectionsPerImage: 'string'
      boxScoreThreshold: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      imageSize: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      maxSize: 'string'
      minSize: 'string'
      modelName: 'string'
      modelSize: 'string'
      momentum: 'string'
      multiScale: 'string'
      nesterov: 'string'
      nmsIouThreshold: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      tileGridSize: 'string'
      tileOverlapRatio: 'string'
      tilePredictionsNmsThreshold: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      validationIouThreshold: 'string'
      validationMetricType: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  taskType: 'ImageInstanceSegmentation'
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
}

For ImageObjectDetection, use:

{
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    boxDetectionsPerImage: int
    boxScoreThreshold: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    imageSize: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    maxSize: int
    minSize: int
    modelName: 'string'
    modelSize: 'string'
    momentum: int
    multiScale: bool
    nesterov: bool
    nmsIouThreshold: int
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    tileGridSize: 'string'
    tileOverlapRatio: int
    tilePredictionsNmsThreshold: int
    trainingBatchSize: int
    validationBatchSize: int
    validationIouThreshold: int
    validationMetricType: 'string'
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      boxDetectionsPerImage: 'string'
      boxScoreThreshold: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      imageSize: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      maxSize: 'string'
      minSize: 'string'
      modelName: 'string'
      modelSize: 'string'
      momentum: 'string'
      multiScale: 'string'
      nesterov: 'string'
      nmsIouThreshold: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      tileGridSize: 'string'
      tileOverlapRatio: 'string'
      tilePredictionsNmsThreshold: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      validationIouThreshold: 'string'
      validationMetricType: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  taskType: 'ImageObjectDetection'
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
}

For Regression, use:

{
  cvSplitColumnNames: [
    'string'
  ]
  featurizationSettings: {
    blockedTransformers: [
      'string'
    ]
    columnNameAndTypes: {
      {customized property}: 'string'
    }
    datasetLanguage: 'string'
    enableDnnFeaturization: bool
    mode: 'string'
    transformerParams: {
      {customized property}: [
        {
          fields: [
            'string'
          ]
          parameters: any(Azure.Bicep.Types.Concrete.AnyType)
        }
      ]
    }
  }
  limitSettings: {
    enableEarlyTermination: bool
    exitScore: int
    maxConcurrentTrials: int
    maxCoresPerTrial: int
    maxTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  nCrossValidations: {
    mode: 'string'
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric: 'string'
  taskType: 'Regression'
  testData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  testDataSize: int
  trainingSettings: {
    allowedTrainingAlgorithms: [
      'string'
    ]
    blockedTrainingAlgorithms: [
      'string'
    ]
    enableDnnTraining: bool
    enableModelExplainability: bool
    enableOnnxCompatibleModels: bool
    enableStackEnsemble: bool
    enableVoteEnsemble: bool
    ensembleModelDownloadTimeout: 'string'
    stackEnsembleSettings: {
      stackMetaLearnerKWargs: any(Azure.Bicep.Types.Concrete.AnyType)
      stackMetaLearnerTrainPercentage: int
      stackMetaLearnerType: 'string'
    }
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
  weightColumnName: 'string'
}

For TextClassification, use:

{
  featurizationSettings: {
    datasetLanguage: 'string'
  }
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  primaryMetric: 'string'
  taskType: 'TextClassification'
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
}

For TextClassificationMultilabel, use:

{
  featurizationSettings: {
    datasetLanguage: 'string'
  }
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  taskType: 'TextClassificationMultilabel'
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
}

For TextNER, use:

{
  featurizationSettings: {
    datasetLanguage: 'string'
  }
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  taskType: 'TextNER'
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
}

EarlyTerminationPolicy objects

Set the policyType property to specify the type of object.

For Bandit, use:

{
  policyType: 'Bandit'
  slackAmount: int
  slackFactor: int
}

For MedianStopping, use:

{
  policyType: 'MedianStopping'
}

For TruncationSelection, use:

{
  policyType: 'TruncationSelection'
  truncationPercentage: int
}

DataQualityMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

{
  dataType: 'Categorical'
  metric: 'string'
}

For Numerical, use:

{
  dataType: 'Numerical'
  metric: 'string'
}

MonitoringSignalBase objects

Set the signalType property to specify the type of object.

For Custom, use:

{
  componentId: 'string'
  inputAssets: {
    {customized property}: {
      columns: {
        {customized property}: 'string'
      }
      dataContext: 'string'
      jobInputType: 'string'
      uri: 'string'
      inputDataType: 'string'
      // For remaining properties, see MonitoringInputDataBase objects
    }
  }
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  metricThresholds: [
    {
      metric: 'string'
      threshold: {
        value: int
      }
    }
  ]
  signalType: 'Custom'
}

For DataDrift, use:

{
  featureDataTypeOverride: {
    {customized property}: 'string'
  }
  featureImportanceSettings: {
    mode: 'string'
    targetColumn: 'string'
  }
  features: {
    filterType: 'string'
    // For remaining properties, see MonitoringFeatureFilterBase objects
  }
  metricThresholds: [
    {
      threshold: {
        value: int
      }
      dataType: 'string'
      // For remaining properties, see DataDriftMetricThresholdBase objects
    }
  ]
  productionData: {
    columns: {
      {customized property}: 'string'
    }
    dataContext: 'string'
    jobInputType: 'string'
    uri: 'string'
    inputDataType: 'string'
    // For remaining properties, see MonitoringInputDataBase objects
  }
  referenceData: {
    columns: {
      {customized property}: 'string'
    }
    dataContext: 'string'
    jobInputType: 'string'
    uri: 'string'
    inputDataType: 'string'
    // For remaining properties, see MonitoringInputDataBase objects
  }
  signalType: 'DataDrift'
}

For DataQuality, use:

{
  featureDataTypeOverride: {
    {customized property}: 'string'
  }
  featureImportanceSettings: {
    mode: 'string'
    targetColumn: 'string'
  }
  features: {
    filterType: 'string'
    // For remaining properties, see MonitoringFeatureFilterBase objects
  }
  metricThresholds: [
    {
      threshold: {
        value: int
      }
      dataType: 'string'
      // For remaining properties, see DataQualityMetricThresholdBase objects
    }
  ]
  productionData: {
    columns: {
      {customized property}: 'string'
    }
    dataContext: 'string'
    jobInputType: 'string'
    uri: 'string'
    inputDataType: 'string'
    // For remaining properties, see MonitoringInputDataBase objects
  }
  referenceData: {
    columns: {
      {customized property}: 'string'
    }
    dataContext: 'string'
    jobInputType: 'string'
    uri: 'string'
    inputDataType: 'string'
    // For remaining properties, see MonitoringInputDataBase objects
  }
  signalType: 'DataQuality'
}

For FeatureAttributionDrift, use:

{
  featureDataTypeOverride: {
    {customized property}: 'string'
  }
  featureImportanceSettings: {
    mode: 'string'
    targetColumn: 'string'
  }
  metricThreshold: {
    metric: 'string'
    threshold: {
      value: int
    }
  }
  productionData: [
    {
      columns: {
        {customized property}: 'string'
      }
      dataContext: 'string'
      jobInputType: 'string'
      uri: 'string'
      inputDataType: 'string'
      // For remaining properties, see MonitoringInputDataBase objects
    }
  ]
  referenceData: {
    columns: {
      {customized property}: 'string'
    }
    dataContext: 'string'
    jobInputType: 'string'
    uri: 'string'
    inputDataType: 'string'
    // For remaining properties, see MonitoringInputDataBase objects
  }
  signalType: 'FeatureAttributionDrift'
}

For PredictionDrift, use:

{
  featureDataTypeOverride: {
    {customized property}: 'string'
  }
  metricThresholds: [
    {
      threshold: {
        value: int
      }
      dataType: 'string'
      // For remaining properties, see PredictionDriftMetricThresholdBase objects
    }
  ]
  productionData: {
    columns: {
      {customized property}: 'string'
    }
    dataContext: 'string'
    jobInputType: 'string'
    uri: 'string'
    inputDataType: 'string'
    // For remaining properties, see MonitoringInputDataBase objects
  }
  referenceData: {
    columns: {
      {customized property}: 'string'
    }
    dataContext: 'string'
    jobInputType: 'string'
    uri: 'string'
    inputDataType: 'string'
    // For remaining properties, see MonitoringInputDataBase objects
  }
  signalType: 'PredictionDrift'
}

Seasonality objects

Set the mode property to specify the type of object.

For Auto, use:

{
  mode: 'Auto'
}

For Custom, use:

{
  mode: 'Custom'
  value: int
}

FineTuningVertical objects

Set the modelProvider property to specify the type of object.

For AzureOpenAI, use:

{
  hyperParameters: {
    batchSize: int
    learningRateMultiplier: int
    nEpochs: int
  }
  modelProvider: 'AzureOpenAI'
}

For Custom, use:

{
  hyperParameters: {
    {customized property}: 'string'
  }
  modelProvider: 'Custom'
}

IdentityConfiguration objects

Set the identityType property to specify the type of object.

For AMLToken, use:

{
  identityType: 'AMLToken'
}

For Managed, use:

{
  clientId: 'string'
  identityType: 'Managed'
  objectId: 'string'
  resourceId: 'string'
}

For UserIdentity, use:

{
  identityType: 'UserIdentity'
}

TargetRollingWindowSize objects

Set the mode property to specify the type of object.

For Auto, use:

{
  mode: 'Auto'
}

For Custom, use:

{
  mode: 'Custom'
  value: int
}

JobOutput objects

Set the jobOutputType property to specify the type of object.

For custom_model, use:

{
  jobOutputType: 'custom_model'
  mode: 'string'
  uri: 'string'
}

For mlflow_model, use:

{
  jobOutputType: 'mlflow_model'
  mode: 'string'
  uri: 'string'
}

For mltable, use:

{
  jobOutputType: 'mltable'
  mode: 'string'
  uri: 'string'
}

For triton_model, use:

{
  jobOutputType: 'triton_model'
  mode: 'string'
  uri: 'string'
}

For uri_file, use:

{
  jobOutputType: 'uri_file'
  mode: 'string'
  uri: 'string'
}

For uri_folder, use:

{
  jobOutputType: 'uri_folder'
  mode: 'string'
  uri: 'string'
}

NCrossValidations objects

Set the mode property to specify the type of object.

For Auto, use:

{
  mode: 'Auto'
}

For Custom, use:

{
  mode: 'Custom'
  value: int
}

MonitoringInputDataBase objects

Set the inputDataType property to specify the type of object.

For Fixed, use:

{
  inputDataType: 'Fixed'
}

For Rolling, use:

{
  inputDataType: 'Rolling'
  preprocessingComponentId: 'string'
  windowOffset: 'string'
  windowSize: 'string'
}

For Static, use:

{
  inputDataType: 'Static'
  preprocessingComponentId: 'string'
  windowEnd: 'string'
  windowStart: 'string'
}

ForecastHorizon objects

Set the mode property to specify the type of object.

For Auto, use:

{
  mode: 'Auto'
}

For Custom, use:

{
  mode: 'Custom'
  value: int
}

SamplingAlgorithm objects

Set the samplingAlgorithmType property to specify the type of object.

For Bayesian, use:

{
  samplingAlgorithmType: 'Bayesian'
}

For Grid, use:

{
  samplingAlgorithmType: 'Grid'
}

For Random, use:

{
  rule: 'string'
  samplingAlgorithmType: 'Random'
  seed: int
}

MonitoringFeatureFilterBase objects

Set the filterType property to specify the type of object.

For AllFeatures, use:

{
  filterType: 'AllFeatures'
}

For FeatureSubset, use:

{
  features: [
    'string'
  ]
  filterType: 'FeatureSubset'
}

For TopNByAttribution, use:

{
  filterType: 'TopNByAttribution'
  top: int
}

MonitorComputeConfigurationBase objects

Set the computeType property to specify the type of object.

For ServerlessSpark, use:

{
  computeIdentity: {
    computeIdentityType: 'string'
    // For remaining properties, see MonitorComputeIdentityBase objects
  }
  computeType: 'ServerlessSpark'
  instanceType: 'string'
  runtimeVersion: 'string'
}

Property values

AllFeatures

Name	Description	Value
filterType	[Required] Specifies the feature filter to leverage when selecting features to calculate metrics over.	'AllFeatures' (required)

AllNodes

Name	Description	Value
nodesValueType	[Required] Type of the Nodes value	'All' (required)

AmlToken

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'AMLToken' (required)

AmlTokenComputeIdentity

Name	Description	Value
computeIdentityType	[Required] Specifies the type of identity to use within the monitoring jobs.	'AmlToken' (required)

AutoForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	'Auto' (required)

AutoMLJob

Name	Description	Value
environmentId	The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.	string
environmentVariables	Environment variables included in the job.	AutoMLJobEnvironmentVariables
jobType	[Required] Specifies the type of job.	'AutoML' (required)
outputs	Mapping of output data bindings used in the job.	AutoMLJobOutputs
queueSettings	Queue settings for the job	QueueSettings
resources	Compute Resource configuration for the job.	JobResourceConfiguration
taskDetails	[Required] This represents scenario which can be one of Tables/NLP/Image	AutoMLVertical (required)

AutoMLJobEnvironmentVariables

Name	Description	Value

AutoMLJobOutputs

Name	Description	Value

AutoMLVertical

Name	Description	Value
logVerbosity	Log verbosity for the job.	'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning'
targetColumnName	Target column name: This is prediction values column. Also known as label column name in context of classification tasks.	string
taskType	Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer.	'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required)
trainingData	[Required] Training data input.	MLTableJobInput (required)

AutoNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	'Auto' (required)

AutoSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	'Auto' (required)

AutoTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	'Auto' (required)

AutoTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	'Auto' (required)

AzureDevOpsWebhook

Name	Description	Value
webhookType	[Required] Specifies the type of service to send a callback	'AzureDevOps' (required)

AzureOpenAiFineTuning

Name	Description	Value
hyperParameters	HyperParameters for fine tuning Azure Open AI model.	AzureOpenAiHyperParameters
modelProvider	[Required] Enum to determine the type of fine tuning.	'AzureOpenAI' (required)

AzureOpenAiHyperParameters

Name	Description	Value
batchSize	Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.	int
learningRateMultiplier	Scaling factor for the learning rate. A smaller learning rate may be useful to avoid over fitting.	int
nEpochs	The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.	int

BanditPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'Bandit' (required)
slackAmount	Absolute distance allowed from the best performing run.	int
slackFactor	Ratio of the allowed distance from the best performing run.	int

BayesianSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Bayesian' (required)

CategoricalDataDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Categorical' (required)
metric	[Required] The categorical data drift metric to calculate.	'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required)

CategoricalDataQualityMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Categorical' (required)
metric	[Required] The categorical data quality metric to calculate.	'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required)

CategoricalPredictionDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Categorical' (required)
metric	[Required] The categorical prediction drift metric to calculate.	'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required)

Classification

Name	Description	Value
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
positiveLabel	Positive label for binary metrics calculation.	string
primaryMetric	Primary metric for the task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
taskType	[Required] Task type for AutoMLJob.	'Classification' (required)
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ClassificationTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

ClassificationTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for classification task.	String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier'
blockedTrainingAlgorithms	Blocked models for classification task.	String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings

ColumnTransformer

Name	Description	Value
fields	Fields to apply transformer logic on.	string[]
parameters	Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.	any

CommandJob

Name	Description	Value
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
environmentVariables	Environment variables included in the job.	CommandJobEnvironmentVariables
inputs	Mapping of input data bindings used in the job.	CommandJobInputs
jobType	[Required] Specifies the type of job.	'Command' (required)
limits	Command Job limit.	CommandJobLimits
outputs	Mapping of output data bindings used in the job.	CommandJobOutputs
queueSettings	Queue settings for the job	QueueSettings
resources	Compute Resource configuration for the job.	JobResourceConfiguration

CommandJobEnvironmentVariables

Name	Description	Value

CommandJobInputs

Name	Description	Value

CommandJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	'Command' 'Sweep' (required)
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string

CommandJobOutputs

Name	Description	Value

CreateMonitorAction

Name	Description	Value
actionType	[Required] Specifies the action type of the schedule	'CreateMonitor' (required)
monitorDefinition	[Required] Defines the monitor.	MonitorDefinition (required)

CronTrigger

Name	Description	Value
expression	[Required] Specifies cron expression of schedule. The expression should follow NCronTab format.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
triggerType	[Required]	'Cron' (required)

CustomForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	'Custom' (required)
value	[Required] Forecast horizon value.	int (required)

CustomMetricThreshold

Name	Description	Value
metric	[Required] The user-defined metric to calculate.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

CustomModelFineTuning

Name	Description	Value
hyperParameters	HyperParameters for fine tuning custom model.	CustomModelFineTuningHyperParameters
modelProvider	[Required] Enum to determine the type of fine tuning.	'Custom' (required)

CustomModelFineTuningHyperParameters

Name	Description	Value

CustomModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'custom_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

CustomModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'custom_model' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

CustomMonitoringSignal

Name	Description	Value
componentId	[Required] Reference to the component asset used to calculate the custom metrics.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
inputAssets	Monitoring assets to take as input. Key is the component input port name, value is the data asset.	CustomMonitoringSignalInputAssets
inputs	Extra component parameters to take as input. Key is the component literal input port name, value is the parameter value.	CustomMonitoringSignalInputs
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	CustomMetricThreshold[] (required)
signalType	[Required] Specifies the type of signal to monitor.	'Custom' (required)

CustomMonitoringSignalInputAssets

Name	Description	Value

CustomMonitoringSignalInputs

Name	Description	Value

CustomNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	'Custom' (required)
value	[Required] N-Cross validations value.	int (required)

CustomSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	'Custom' (required)
value	[Required] Seasonality value.	int (required)

CustomTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	'Custom' (required)
values	[Required] Set target lags values.	int[] (required)

CustomTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	'Custom' (required)
value	[Required] TargetRollingWindowSize value.	int (required)

DataDriftMetricThresholdBase

Name	Description	Value
dataType	Set to 'Categorical' for type CategoricalDataDriftMetricThreshold. Set to 'Numerical' for type NumericalDataDriftMetricThreshold.	'Categorical' 'Numerical' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

DataDriftMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	DataDriftMonitoringSignalFeatureDataTypeOverride
featureImportanceSettings	The settings for computing feature importance.	FeatureImportanceSettings
features	The feature filter which identifies which feature to calculate drift over.	MonitoringFeatureFilterBase
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	DataDriftMetricThresholdBase[] (required)
productionData	[Required] The data which drift will be calculated for.	MonitoringInputDataBase (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'DataDrift' (required)

DataDriftMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

DataQualityMetricThresholdBase

Name	Description	Value
dataType	Set to 'Categorical' for type CategoricalDataQualityMetricThreshold. Set to 'Numerical' for type NumericalDataQualityMetricThreshold.	'Categorical' 'Numerical' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

DataQualityMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	DataQualityMonitoringSignalFeatureDataTypeOverride
featureImportanceSettings	The settings for computing feature importance.	FeatureImportanceSettings
features	The features to calculate drift over.	MonitoringFeatureFilterBase
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	DataQualityMetricThresholdBase[] (required)
productionData	[Required] The data produced by the production service which drift will be calculated for.	MonitoringInputDataBase (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'DataQuality' (required)

DataQualityMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

DistributionConfiguration

Name	Description	Value
distributionType	Set to 'Mpi' for type Mpi. Set to 'PyTorch' for type PyTorch. Set to 'TensorFlow' for type TensorFlow.	'Mpi' 'PyTorch' 'TensorFlow' (required)

EarlyTerminationPolicy

Name	Description	Value
delayEvaluation	Number of intervals by which to delay the first evaluation.	int
evaluationInterval	Interval (number of runs) between policy evaluations.	int
policyType	Set to 'Bandit' for type BanditPolicy. Set to 'MedianStopping' for type MedianStoppingPolicy. Set to 'TruncationSelection' for type TruncationSelectionPolicy.	'Bandit' 'MedianStopping' 'TruncationSelection' (required)

EndpointScheduleAction

Name	Description	Value
actionType	[Required] Specifies the action type of the schedule	'InvokeBatchEndpoint' (required)
endpointInvocationDefinition	[Required] Defines Schedule action definition details. <see href="TBD" />	any (required)

FeatureAttributionDriftMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride
featureImportanceSettings	[Required] The settings for computing feature importance.	FeatureImportanceSettings (required)
metricThreshold	[Required] A list of metrics to calculate and their associated thresholds.	FeatureAttributionMetricThreshold (required)
productionData	[Required] The data which drift will be calculated for.	MonitoringInputDataBase[] (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'FeatureAttributionDrift' (required)

FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

FeatureAttributionMetricThreshold

Name	Description	Value
metric	[Required] The feature attribution metric to calculate.	'NormalizedDiscountedCumulativeGain' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

FeatureImportanceSettings

Name	Description	Value
mode	The mode of operation for computing feature importance.	'Disabled' 'Enabled'
targetColumn	The name of the target column within the input data asset.	string

FeatureSubset

Name	Description	Value
features	[Required] The list of features to include.	string[] (required)
filterType	[Required] Specifies the feature filter to leverage when selecting features to calculate metrics over.	'FeatureSubset' (required)

FineTuningJob

Name	Description	Value
fineTuningDetails	[Required]	FineTuningVertical (required)
jobType	[Required] Specifies the type of job.	'FineTuning' (required)
outputs	[Required]	FineTuningJobOutputs (required)
queueSettings	Queue settings for the job	QueueSettings
resources	Instance types and other resources for the job	JobResources

FineTuningJobOutputs

Name	Description	Value

FineTuningVertical

Name	Description	Value
model	[Required] Input model for fine tuning.	MLFlowModelJobInput (required)
modelProvider	Set to 'AzureOpenAI' for type AzureOpenAiFineTuning. Set to 'Custom' for type CustomModelFineTuning.	'AzureOpenAI' 'Custom' (required)
taskType	[Required] Fine tuning task type.	'ChatCompletion' 'ImageClassification' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'QuestionAnswering' 'TextClassification' 'TextCompletion' 'TextSummarization' 'TextTranslation' 'TokenClassification' 'VideoMultiObjectTracking' (required)
trainingData	[Required] Training data for fine tuning.	JobInput (required)
validationData	Validation data for fine tuning.	JobInput

FixedInputData

Name	Description	Value
inputDataType	[Required] Specifies the type of signal to monitor.	'Fixed' (required)

ForecastHorizon

Name	Description	Value
mode	Set to 'Auto' for type AutoForecastHorizon. Set to 'Custom' for type CustomForecastHorizon.	'Auto' 'Custom' (required)

Forecasting

Name	Description	Value
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
forecastingSettings	Forecasting task specific inputs.	ForecastingSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for forecasting task.	'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation'
taskType	[Required] Task type for AutoMLJob.	'Forecasting' (required)
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ForecastingTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

ForecastingSettings

Name	Description	Value
countryOrRegionForHolidays	Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.	string
cvStepSize	Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.	int
featureLags	Flag for generating lags for the numeric features with 'auto' or null.	'Auto' 'None'
forecastHorizon	The desired maximum forecast horizon in units of time-series frequency.	ForecastHorizon
frequency	When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.	string
seasonality	Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.	Seasonality
shortSeriesHandlingConfig	The parameter defining how if AutoML should handle short time series.	'Auto' 'Drop' 'None' 'Pad'
targetAggregateFunction	The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".	'Max' 'Mean' 'Min' 'None' 'Sum'
targetLags	The number of past periods to lag from the target column.	TargetLags
targetRollingWindowSize	The number of past periods used to create a rolling window average of the target column.	TargetRollingWindowSize
timeColumnName	The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.	string
timeSeriesIdColumnNames	The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.	string[]
useStl	Configure STL Decomposition of the time-series target column.	'None' 'Season' 'SeasonTrend'

ForecastingTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for forecasting task.	String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor'
blockedTrainingAlgorithms	Blocked models for forecasting task.	String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings

GridSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Grid' (required)

IdentityConfiguration

Name	Description	Value
identityType	Set to 'AMLToken' for type AmlToken. Set to 'Managed' for type ManagedIdentity. Set to 'UserIdentity' for type UserIdentity.	'AMLToken' 'Managed' 'UserIdentity' (required)

ImageClassification

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageClassification' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageClassificationMultilabel

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'IOU' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageClassificationMultilabel' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageInstanceSegmentation

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	'MeanAveragePrecision'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageInstanceSegmentation' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum number of concurrent AutoML iterations.	int
maxTrials	Maximum number of AutoML iterations.	int
timeout	AutoML job timeout.	string

ImageModelDistributionSettingsClassification

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	string
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	string

ImageModelDistributionSettingsObjectDetection

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	string
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	string
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	string
validationMetricType	Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string

ImageModelSettingsClassification

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	'None' 'Step' 'WarmupCosine'
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	'Adam' 'Adamw' 'None' 'Sgd'
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	int
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	int
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	int

ImageModelSettingsObjectDetection

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	int
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	'None' 'Step' 'WarmupCosine'
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	'ExtraLarge' 'Large' 'Medium' 'None' 'Small'
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	bool
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].	int
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	'Adam' 'Adamw' 'None' 'Sgd'
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	int
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	int
validationMetricType	Metric computation method to use for validation metrics.	'Coco' 'CocoVoc' 'None' 'Voc'
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int

ImageObjectDetection

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	'MeanAveragePrecision'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageObjectDetection' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of the hyperparameter sampling algorithms.	'Bayesian' 'Grid' 'Random' (required)

JobBaseProperties

Name	Description	Value
componentId	ARM resource ID of the component resource.	string
computeId	ARM resource ID of the compute resource.	string
description	The asset description text.	string
displayName	Display name of job.	string
experimentName	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.	string
identity	Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.	IdentityConfiguration
isArchived	Is the asset archived?	bool
jobType	Set to 'AutoML' for type AutoMLJob. Set to 'Command' for type CommandJob. Set to 'FineTuning' for type FineTuningJob. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob.	'AutoML' 'Command' 'FineTuning' 'Pipeline' 'Spark' 'Sweep' (required)
notificationSetting	Notification setting for the job	NotificationSetting
properties	The asset property dictionary.	ResourceBaseProperties
services	List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.	JobBaseServices
tags	Tag dictionary. Tags can be added, removed, and updated.	ResourceBaseTags

JobBaseServices

Name	Description	Value

JobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	Set to 'custom_model' for type CustomModelJobInput. Set to 'literal' for type LiteralJobInput. Set to 'mlflow_model' for type MLFlowModelJobInput. Set to 'mltable' for type MLTableJobInput. Set to 'triton_model' for type TritonModelJobInput. Set to 'uri_file' for type UriFileJobInput. Set to 'uri_folder' for type UriFolderJobInput.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)

JobOutput

Name	Description	Value
description	Description for the output.	string
jobOutputType	Set to 'custom_model' for type CustomModelJobOutput. Set to 'mlflow_model' for type MLFlowModelJobOutput. Set to 'mltable' for type MLTableJobOutput. Set to 'triton_model' for type TritonModelJobOutput. Set to 'uri_file' for type UriFileJobOutput. Set to 'uri_folder' for type UriFolderJobOutput.	'custom_model' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)

JobResourceConfiguration

Name	Description	Value
dockerArgs	Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.	string
instanceCount	Optional number of instances or nodes used by the compute target.	int
instanceType	Optional type of VM used as supported by the compute target.	string
properties	Additional properties bag.	ResourceConfigurationProperties
shmSize	Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).	string Constraints: Pattern = \d+[bBkKmMgG]

JobResources

Name	Description	Value
instanceTypes	List of instance types to choose from.	string[]

JobScheduleAction

Name	Description	Value
actionType	[Required] Specifies the action type of the schedule	'CreateJob' (required)
jobDefinition	[Required] Defines Schedule action definition details.	JobBaseProperties (required)

JobService

Name	Description	Value
endpoint	Url for endpoint.	string
jobServiceType	Endpoint type.	string
nodes	Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.	Nodes
port	Port for endpoint.	int
properties	Additional properties to set on the endpoint.	JobServiceProperties

JobServiceProperties

Name	Description	Value

LiteralJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'literal' (required)
value	[Required] Literal value for the input.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

ManagedComputeIdentity

Name	Description	Value
computeIdentityType	[Required] Specifies the type of identity to use within the monitoring jobs.	'ManagedIdentity' (required)
identity	The identity which will be leveraged by the monitoring jobs.	ManagedServiceIdentity

ManagedIdentity

Name	Description	Value
clientId	Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
identityType	[Required] Specifies the type of identity framework.	'Managed' (required)
objectId	Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
resourceId	Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.	string

ManagedServiceIdentity

Name	Description	Value
type	Type of managed service identity (where both SystemAssigned and UserAssigned types are allowed).	'None' 'SystemAssigned' 'SystemAssigned,UserAssigned' 'UserAssigned' (required)
userAssignedIdentities	The set of user assigned identities associated with the resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form: '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}. The dictionary values can be empty objects ({}) in requests.	UserAssignedIdentities

MedianStoppingPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'MedianStopping' (required)

Microsoft.MachineLearningServices/workspaces/schedules

Name	Description	Value
name	The resource name	string Constraints: Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (required)
parent	In Bicep, you can specify the parent resource for a child resource. You only need to add this property when the child resource is declared outside of the parent resource. For more information, see Child resource outside parent resource.	Symbolic name for resource of type: workspaces
properties	[Required] Additional attributes of the entity.	ScheduleProperties (required)

MLFlowModelJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLFlowModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'mlflow_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLFlowModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'mlflow_model' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

MLTableJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLTableJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'mltable' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLTableJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'mltable' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

MonitorComputeConfigurationBase

Name	Description	Value
computeType	Set to 'ServerlessSpark' for type MonitorServerlessSparkCompute.	'ServerlessSpark' (required)

MonitorComputeIdentityBase

Name	Description	Value
computeIdentityType	Set to 'AmlToken' for type AmlTokenComputeIdentity. Set to 'ManagedIdentity' for type ManagedComputeIdentity.	'AmlToken' 'ManagedIdentity' (required)

MonitorDefinition

Name	Description	Value
alertNotificationSettings	The monitor's notification settings.	MonitorNotificationSettings
computeConfiguration	[Required] The ARM resource ID of the compute resource to run the monitoring job on.	MonitorComputeConfigurationBase (required)
monitoringTarget	The entities targeted by the monitor.	MonitoringTarget
signals	[Required] The signals to monitor.	MonitorDefinitionSignals (required)

MonitorDefinitionSignals

Name	Description	Value

MonitorEmailNotificationSettings

Name	Description	Value
emails	The email recipient list which has a limitation of 499 characters in total.	string[]

MonitoringFeatureFilterBase

Name	Description	Value
filterType	Set to 'AllFeatures' for type AllFeatures. Set to 'FeatureSubset' for type FeatureSubset. Set to 'TopNByAttribution' for type TopNFeaturesByAttribution.	'AllFeatures' 'FeatureSubset' 'TopNByAttribution' (required)

MonitoringInputDataBase

Name	Description	Value
columns	Mapping of column names to special uses.	MonitoringInputDataBaseColumns
dataContext	The context metadata of the data source.	string
inputDataType	Set to 'Fixed' for type FixedInputData. Set to 'Rolling' for type RollingInputData. Set to 'Static' for type StaticInputData.	'Fixed' 'Rolling' 'Static' (required)
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MonitoringInputDataBaseColumns

Name	Description	Value

MonitoringSignalBase

Name	Description	Value
notificationTypes	The current notification mode for this signal.	String array containing any of: 'AmlNotification'
properties	Property dictionary. Properties can be added, but not removed or altered.	MonitoringSignalBaseProperties
signalType	Set to 'Custom' for type CustomMonitoringSignal. Set to 'DataDrift' for type DataDriftMonitoringSignal. Set to 'DataQuality' for type DataQualityMonitoringSignal. Set to 'FeatureAttributionDrift' for type FeatureAttributionDriftMonitoringSignal. Set to 'PredictionDrift' for type PredictionDriftMonitoringSignal.	'Custom' 'DataDrift' 'DataQuality' 'FeatureAttributionDrift' 'PredictionDrift' (required)

MonitoringSignalBaseProperties

Name	Description	Value

MonitoringTarget

Name	Description	Value
deploymentId	Reference to the deployment asset targeted by this monitor.	string
modelId	Reference to the model asset targeted by this monitor.	string
taskType	[Required] The machine learning task type of the monitored model.	'Classification' 'Regression' (required)

MonitoringThreshold

Name	Description	Value
value	The threshold value. If null, the set default is dependent on the metric type.	int

MonitorNotificationSettings

Name	Description	Value
emailNotificationSettings	The AML notification email settings.	MonitorEmailNotificationSettings

MonitorServerlessSparkCompute

Name	Description	Value
computeIdentity	[Required] The identity scheme leveraged to by the spark jobs running on serverless Spark.	MonitorComputeIdentityBase (required)
computeType	[Required] Specifies the type of signal to monitor.	'ServerlessSpark' (required)
instanceType	[Required] The instance type running the Spark job.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
runtimeVersion	[Required] The Spark runtime version.	string Constraints: Min length = 1 Pattern = ^[0-9]+\.[0-9]+$ (required)

Mpi

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'Mpi' (required)
processCountPerInstance	Number of processes per MPI node.	int

NCrossValidations

Name	Description	Value
mode	Set to 'Auto' for type AutoNCrossValidations. Set to 'Custom' for type CustomNCrossValidations.	'Auto' 'Custom' (required)

NlpVerticalFeaturizationSettings

Name	Description	Value
datasetLanguage	Dataset language, useful for the text data.	string

NlpVerticalLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum Concurrent AutoML iterations.	int
maxTrials	Number of AutoML iterations.	int
timeout	AutoML job timeout.	string

Nodes

Name	Description	Value
nodesValueType	Set to 'All' for type AllNodes.	'All' (required)

NotificationSetting

Name	Description	Value
emailOn	Send email notification to user on specified notification type	String array containing any of: 'JobCancelled' 'JobCompleted' 'JobFailed'
emails	This is the email recipient list which has a limitation of 499 characters in total concat with comma separator	string[]
webhooks	Send webhook callback to a service. Key is a user-provided name for the webhook.	NotificationSettingWebhooks

NotificationSettingWebhooks

Name	Description	Value

NumericalDataDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Numerical' (required)
metric	[Required] The numerical data drift metric to calculate.	'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required)

NumericalDataQualityMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Numerical' (required)
metric	[Required] The numerical data quality metric to calculate.	'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required)

NumericalPredictionDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Numerical' (required)
metric	[Required] The numerical prediction drift metric to calculate.	'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required)

Objective

Name	Description	Value
goal	[Required] Defines supported metric goals for hyperparameter tuning	'Maximize' 'Minimize' (required)
primaryMetric	[Required] Name of the metric to optimize.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

PipelineJob

Name	Description	Value
inputs	Inputs for the pipeline job.	PipelineJobInputs
jobs	Jobs construct the Pipeline Job.	PipelineJobJobs
jobType	[Required] Specifies the type of job.	'Pipeline' (required)
outputs	Outputs for the pipeline job	PipelineJobOutputs
settings	Pipeline settings, for things like ContinueRunOnStepFailure etc.	any
sourceJobId	ARM resource ID of source job.	string

PipelineJobInputs

Name	Description	Value

PipelineJobJobs

Name	Description	Value

PipelineJobOutputs

Name	Description	Value

PredictionDriftMetricThresholdBase

Name	Description	Value
dataType	Set to 'Categorical' for type CategoricalPredictionDriftMetricThreshold. Set to 'Numerical' for type NumericalPredictionDriftMetricThreshold.	'Categorical' 'Numerical' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

PredictionDriftMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	PredictionDriftMonitoringSignalFeatureDataTypeOverride
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	PredictionDriftMetricThresholdBase[] (required)
productionData	[Required] The data which drift will be calculated for.	MonitoringInputDataBase (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'PredictionDrift' (required)

PredictionDriftMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

PyTorch

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'PyTorch' (required)
processCountPerInstance	Number of processes per node.	int

QueueSettings

Name	Description	Value
jobTier	Controls the compute job tier	'Basic' 'Null' 'Premium' 'Spot' 'Standard'

RandomSamplingAlgorithm

Name	Description	Value
rule	The specific type of random algorithm	'Random' 'Sobol'
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Random' (required)
seed	An optional integer to use as the seed for random number generation	int

RecurrenceSchedule

Name	Description	Value
hours	[Required] List of hours for the schedule.	int[] (required)
minutes	[Required] List of minutes for the schedule.	int[] (required)
monthDays	List of month days for the schedule	int[]
weekDays	List of days for the schedule.	String array containing any of: 'Friday' 'Monday' 'Saturday' 'Sunday' 'Thursday' 'Tuesday' 'Wednesday'

RecurrenceTrigger

Name	Description	Value
frequency	[Required] The frequency to trigger schedule.	'Day' 'Hour' 'Minute' 'Month' 'Week' (required)
interval	[Required] Specifies schedule interval in conjunction with frequency	int (required)
schedule	The recurrence schedule.	RecurrenceSchedule
triggerType	[Required]	'Recurrence' (required)

Regression

Name	Description	Value
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for regression task.	'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation'
taskType	[Required] Task type for AutoMLJob.	'Regression' (required)
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	RegressionTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

RegressionTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for regression task.	String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor'
blockedTrainingAlgorithms	Blocked models for regression task.	String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings

ResourceBaseProperties

Name	Description	Value

ResourceBaseProperties

Name	Description	Value

ResourceBaseTags

Name	Description	Value

ResourceBaseTags

Name	Description	Value

ResourceConfigurationProperties

Name	Description	Value

RollingInputData

Name	Description	Value
inputDataType	[Required] Specifies the type of signal to monitor.	'Rolling' (required)
preprocessingComponentId	Reference to the component asset used to preprocess the data.	string
windowOffset	[Required] The time offset between the end of the data window and the monitor's current run time.	string (required)
windowSize	[Required] The size of the rolling data window.	string (required)

SamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	Set to 'Bayesian' for type BayesianSamplingAlgorithm. Set to 'Grid' for type GridSamplingAlgorithm. Set to 'Random' for type RandomSamplingAlgorithm.	'Bayesian' 'Grid' 'Random' (required)

ScheduleActionBase

Name	Description	Value
actionType	Set to 'CreateJob' for type JobScheduleAction. Set to 'CreateMonitor' for type CreateMonitorAction. Set to 'InvokeBatchEndpoint' for type EndpointScheduleAction.	'CreateJob' 'CreateMonitor' 'InvokeBatchEndpoint' (required)

ScheduleProperties

Name	Description	Value
action	[Required] Specifies the action of the schedule	ScheduleActionBase (required)
description	The asset description text.	string
displayName	Display name of schedule.	string
isEnabled	Is the schedule enabled?	bool
properties	The asset property dictionary.	ResourceBaseProperties
tags	Tag dictionary. Tags can be added, removed, and updated.	ResourceBaseTags
trigger	[Required] Specifies the trigger details	TriggerBase (required)

Seasonality

Name	Description	Value
mode	Set to 'Auto' for type AutoSeasonality. Set to 'Custom' for type CustomSeasonality.	'Auto' 'Custom' (required)

SparkJob

Name	Description	Value
archives	Archive files used in the job.	string[]
args	Arguments for the job.	string
codeId	[Required] arm-id of the code asset.	string (required)
conf	Spark configured properties.	SparkJobConf
entry	[Required] The entry to execute on startup of the job.	SparkJobEntry (required)
environmentId	The ARM resource ID of the Environment specification for the job.	string
environmentVariables	Environment variables included in the job.	SparkJobEnvironmentVariables
files	Files used in the job.	string[]
inputs	Mapping of input data bindings used in the job.	SparkJobInputs
jars	Jar files used in the job.	string[]
jobType	[Required] Specifies the type of job.	'Spark' (required)
outputs	Mapping of output data bindings used in the job.	SparkJobOutputs
pyFiles	Python files used in the job.	string[]
queueSettings	Queue settings for the job	QueueSettings
resources	Compute Resource configuration for the job.	SparkResourceConfiguration

SparkJobConf

Name	Description	Value

SparkJobEntry

Name	Description	Value
sparkJobEntryType	Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry.	'SparkJobPythonEntry' 'SparkJobScalaEntry' (required)

SparkJobEnvironmentVariables

Name	Description	Value

SparkJobInputs

Name	Description	Value

SparkJobOutputs

Name	Description	Value

SparkJobPythonEntry

Name	Description	Value
file	[Required] Relative python file path for job entry point.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
sparkJobEntryType	[Required] Type of the job's entry point.	'SparkJobPythonEntry' (required)

SparkJobScalaEntry

Name	Description	Value
className	[Required] Scala class name used as entry point.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
sparkJobEntryType	[Required] Type of the job's entry point.	'SparkJobScalaEntry' (required)

SparkResourceConfiguration

Name	Description	Value
instanceType	Optional type of VM used as supported by the compute target.	string
runtimeVersion	Version of spark runtime used for the job.	string

StackEnsembleSettings

Name	Description	Value
stackMetaLearnerKWargs	Optional parameters to pass to the initializer of the meta-learner.	any
stackMetaLearnerTrainPercentage	Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.	int
stackMetaLearnerType	The meta-learner is a model trained on the output of the individual heterogeneous models.	'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None'

StaticInputData

Name	Description	Value
inputDataType	[Required] Specifies the type of signal to monitor.	'Static' (required)
preprocessingComponentId	Reference to the component asset used to preprocess the data.	string
windowEnd	[Required] The end date of the data window.	string (required)
windowStart	[Required] The start date of the data window.	string (required)

SweepJob

Name	Description	Value
earlyTermination	Early termination policies enable canceling poor-performing runs before they complete	EarlyTerminationPolicy
inputs	Mapping of input data bindings used in the job.	SweepJobInputs
jobType	[Required] Specifies the type of job.	'Sweep' (required)
limits	Sweep Job limit.	SweepJobLimits
objective	[Required] Optimization objective.	Objective (required)
outputs	Mapping of output data bindings used in the job.	SweepJobOutputs
queueSettings	Queue settings for the job	QueueSettings
samplingAlgorithm	[Required] The hyperparameter sampling algorithm	SamplingAlgorithm (required)
searchSpace	[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter	any (required)
trial	[Required] Trial component definition.	TrialComponent (required)

SweepJobInputs

Name	Description	Value

SweepJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	'Command' 'Sweep' (required)
maxConcurrentTrials	Sweep Job max concurrent trials.	int
maxTotalTrials	Sweep Job max total trials.	int
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string
trialTimeout	Sweep Job Trial timeout value.	string

SweepJobOutputs

Name	Description	Value

TableVerticalFeaturizationSettings

Name	Description	Value
blockedTransformers	These transformers shall not be used in featurization.	String array containing any of: 'CatTargetEncoder' 'CountVectorizer' 'HashOneHotEncoder' 'LabelEncoder' 'NaiveBayes' 'OneHotEncoder' 'TextTargetEncoder' 'TfIdf' 'WoETargetEncoder' 'WordEmbedding'
columnNameAndTypes	Dictionary of column name and its type (int, float, string, datetime etc).	TableVerticalFeaturizationSettingsColumnNameAndTypes
datasetLanguage	Dataset language, useful for the text data.	string
enableDnnFeaturization	Determines whether to use Dnn based featurizers for data featurization.	bool
mode	Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.	'Auto' 'Custom' 'Off'
transformerParams	User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.	TableVerticalFeaturizationSettingsTransformerParams

TableVerticalFeaturizationSettingsColumnNameAndTypes

Name	Description	Value

TableVerticalFeaturizationSettingsTransformerParams

Name	Description	Value

TableVerticalLimitSettings

Name	Description	Value
enableEarlyTermination	Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.	bool
exitScore	Exit score for the AutoML job.	int
maxConcurrentTrials	Maximum Concurrent iterations.	int
maxCoresPerTrial	Max cores per iteration.	int
maxTrials	Number of iterations.	int
timeout	AutoML job timeout.	string
trialTimeout	Iteration timeout.	string

TargetLags

Name	Description	Value
mode	Set to 'Auto' for type AutoTargetLags. Set to 'Custom' for type CustomTargetLags.	'Auto' 'Custom' (required)

TargetRollingWindowSize

Name	Description	Value
mode	Set to 'Auto' for type AutoTargetRollingWindowSize. Set to 'Custom' for type CustomTargetRollingWindowSize.	'Auto' 'Custom' (required)

TensorFlow

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'TensorFlow' (required)
parameterServerCount	Number of parameter server tasks.	int
workerCount	Number of workers. If not specified, will default to the instance count.	int

TextClassification

Name	Description	Value
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
primaryMetric	Primary metric for Text-Classification task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
taskType	[Required] Task type for AutoMLJob.	'TextClassification' (required)
validationData	Validation data inputs.	MLTableJobInput

TextClassificationMultilabel

Name	Description	Value
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
taskType	[Required] Task type for AutoMLJob.	'TextClassificationMultilabel' (required)
validationData	Validation data inputs.	MLTableJobInput

TextNer

Name	Description	Value
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
taskType	[Required] Task type for AutoMLJob.	'TextNER' (required)
validationData	Validation data inputs.	MLTableJobInput

TopNFeaturesByAttribution

Name	Description	Value
filterType	[Required] Specifies the feature filter to leverage when selecting features to calculate metrics over.	'TopNByAttribution' (required)
top	The number of top features to include.	int

TrialComponent

Name	Description	Value
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
environmentVariables	Environment variables included in the job.	TrialComponentEnvironmentVariables
resources	Compute Resource configuration for the job.	JobResourceConfiguration

TrialComponentEnvironmentVariables

Name	Description	Value

TriggerBase

Name	Description	Value
endTime	Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely	string
startTime	Specifies start time of schedule in ISO 8601 format, but without a UTC offset.	string
timeZone	Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones?view=windows-11	string
triggerType	Set to 'Cron' for type CronTrigger. Set to 'Recurrence' for type RecurrenceTrigger.	'Cron' 'Recurrence' (required)

TritonModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'triton_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

TritonModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'triton_model' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

TruncationSelectionPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'TruncationSelection' (required)
truncationPercentage	The percentage of runs to cancel at each evaluation interval.	int

UriFileJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'uri_file' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

UriFileJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'uri_file' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

UriFolderJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

UriFolderJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'uri_folder' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

UserAssignedIdentities

Name	Description	Value

UserAssignedIdentity

Name	Description	Value

UserIdentity

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'UserIdentity' (required)

Webhook

Name	Description	Value
eventType	Send callback on a specified notification event	string
webhookType	Set to 'AzureDevOps' for type AzureDevOpsWebhook.	'AzureDevOps' (required)

ARM template resource definition

The workspaces/schedules resource type can be deployed with operations that target:

• Resource groups - See resource group deployment commands

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/schedules resource, add the following JSON to your template.

{
  "type": "Microsoft.MachineLearningServices/workspaces/schedules",
  "apiVersion": "2024-07-01-preview",
  "name": "string",
  "properties": {
    "action": {
      "actionType": "string"
      // For remaining properties, see ScheduleActionBase objects
    },
    "description": "string",
    "displayName": "string",
    "isEnabled": "bool",
    "properties": {
      "{customized property}": "string"
    },
    "tags": {
      "{customized property}": "string"
    },
    "trigger": {
      "endTime": "string",
      "startTime": "string",
      "timeZone": "string",
      "triggerType": "string"
      // For remaining properties, see TriggerBase objects
    }
  }
}

TriggerBase objects

Set the triggerType property to specify the type of object.

For Cron, use:

{
  "expression": "string",
  "triggerType": "Cron"
}

For Recurrence, use:

{
  "frequency": "string",
  "interval": "int",
  "schedule": {
    "hours": [ "int" ],
    "minutes": [ "int" ],
    "monthDays": [ "int" ],
    "weekDays": [ "string" ]
  },
  "triggerType": "Recurrence"
}

JobInput objects

Set the jobInputType property to specify the type of object.

For custom_model, use:

{
  "jobInputType": "custom_model",
  "mode": "string",
  "uri": "string"
}

For literal, use:

{
  "jobInputType": "literal",
  "value": "string"
}

For mlflow_model, use:

{
  "jobInputType": "mlflow_model",
  "mode": "string",
  "uri": "string"
}

For mltable, use:

{
  "jobInputType": "mltable",
  "mode": "string",
  "uri": "string"
}

For triton_model, use:

{
  "jobInputType": "triton_model",
  "mode": "string",
  "uri": "string"
}

For uri_file, use:

{
  "jobInputType": "uri_file",
  "mode": "string",
  "uri": "string"
}

For uri_folder, use:

{
  "jobInputType": "uri_folder",
  "mode": "string",
  "uri": "string"
}

ScheduleActionBase objects

Set the actionType property to specify the type of object.

For CreateJob, use:

{
  "actionType": "CreateJob",
  "jobDefinition": {
    "componentId": "string",
    "computeId": "string",
    "description": "string",
    "displayName": "string",
    "experimentName": "string",
    "identity": {
      "identityType": "string"
      // For remaining properties, see IdentityConfiguration objects
    },
    "isArchived": "bool",
    "notificationSetting": {
      "emailOn": [ "string" ],
      "emails": [ "string" ],
      "webhooks": {
        "{customized property}": {
          "eventType": "string",
          "webhookType": "string"
          // For remaining properties, see Webhook objects
        }
      }
    },
    "properties": {
      "{customized property}": "string"
    },
    "services": {
      "{customized property}": {
        "endpoint": "string",
        "jobServiceType": "string",
        "nodes": {
          "nodesValueType": "string"
          // For remaining properties, see Nodes objects
        },
        "port": "int",
        "properties": {
          "{customized property}": "string"
        }
      }
    },
    "tags": {
      "{customized property}": "string"
    },
    "jobType": "string"
    // For remaining properties, see JobBaseProperties objects
  }
}

For CreateMonitor, use:

{
  "actionType": "CreateMonitor",
  "monitorDefinition": {
    "alertNotificationSettings": {
      "emailNotificationSettings": {
        "emails": [ "string" ]
      }
    },
    "computeConfiguration": {
      "computeType": "string"
      // For remaining properties, see MonitorComputeConfigurationBase objects
    },
    "monitoringTarget": {
      "deploymentId": "string",
      "modelId": "string",
      "taskType": "string"
    },
    "signals": {
      "{customized property}": {
        "notificationTypes": [ "string" ],
        "properties": {
          "{customized property}": "string"
        },
        "signalType": "string"
        // For remaining properties, see MonitoringSignalBase objects
      }
    }
  }
}

For InvokeBatchEndpoint, use:

{
  "actionType": "InvokeBatchEndpoint",
  "endpointInvocationDefinition": {}
}

Nodes objects

Set the nodesValueType property to specify the type of object.

For All, use:

{
  "nodesValueType": "All"
}

DistributionConfiguration objects

Set the distributionType property to specify the type of object.

For Mpi, use:

{
  "distributionType": "Mpi",
  "processCountPerInstance": "int"
}

For PyTorch, use:

{
  "distributionType": "PyTorch",
  "processCountPerInstance": "int"
}

For TensorFlow, use:

{
  "distributionType": "TensorFlow",
  "parameterServerCount": "int",
  "workerCount": "int"
}

Webhook objects

Set the webhookType property to specify the type of object.

For AzureDevOps, use:

{
  "webhookType": "AzureDevOps"
}

TargetLags objects

Set the mode property to specify the type of object.

For Auto, use:

{
  "mode": "Auto"
}

For Custom, use:

{
  "mode": "Custom",
  "values": [ "int" ]
}

PredictionDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

{
  "dataType": "Categorical",
  "metric": "string"
}

For Numerical, use:

{
  "dataType": "Numerical",
  "metric": "string"
}

DataDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

{
  "dataType": "Categorical",
  "metric": "string"
}

For Numerical, use:

{
  "dataType": "Numerical",
  "metric": "string"
}

MonitorComputeIdentityBase objects

Set the computeIdentityType property to specify the type of object.

For AmlToken, use:

{
  "computeIdentityType": "AmlToken"
}

For ManagedIdentity, use:

{
  "computeIdentityType": "ManagedIdentity",
  "identity": {
    "type": "string",
    "userAssignedIdentities": {
      "{customized property}": {
      }
    }
  }
}

SparkJobEntry objects

Set the sparkJobEntryType property to specify the type of object.

For SparkJobPythonEntry, use:

{
  "file": "string",
  "sparkJobEntryType": "SparkJobPythonEntry"
}

For SparkJobScalaEntry, use:

{
  "className": "string",
  "sparkJobEntryType": "SparkJobScalaEntry"
}

JobBaseProperties objects

Set the jobType property to specify the type of object.

For AutoML, use:

{
  "environmentId": "string",
  "environmentVariables": {
    "{customized property}": "string"
  },
  "jobType": "AutoML",
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "queueSettings": {
    "jobTier": "string"
  },
  "resources": {
    "dockerArgs": "string",
    "instanceCount": "int",
    "instanceType": "string",
    "properties": {
      "{customized property}": {}
    },
    "shmSize": "string"
  },
  "taskDetails": {
    "logVerbosity": "string",
    "targetColumnName": "string",
    "trainingData": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "taskType": "string"
    // For remaining properties, see AutoMLVertical objects
  }
}

For Command, use:

{
  "codeId": "string",
  "command": "string",
  "distribution": {
    "distributionType": "string"
    // For remaining properties, see DistributionConfiguration objects
  },
  "environmentId": "string",
  "environmentVariables": {
    "{customized property}": "string"
  },
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "jobType": "Command",
  "limits": {
    "jobLimitsType": "string",
    "timeout": "string"
  },
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "queueSettings": {
    "jobTier": "string"
  },
  "resources": {
    "dockerArgs": "string",
    "instanceCount": "int",
    "instanceType": "string",
    "properties": {
      "{customized property}": {}
    },
    "shmSize": "string"
  }
}

For FineTuning, use:

{
  "fineTuningDetails": {
    "model": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "taskType": "string",
    "trainingData": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    },
    "validationData": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    },
    "modelProvider": "string"
    // For remaining properties, see FineTuningVertical objects
  },
  "jobType": "FineTuning",
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "queueSettings": {
    "jobTier": "string"
  },
  "resources": {
    "instanceTypes": [ "string" ]
  }
}

For Pipeline, use:

{
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "jobs": {
    "{customized property}": {}
  },
  "jobType": "Pipeline",
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "settings": {},
  "sourceJobId": "string"
}

For Spark, use:

{
  "archives": [ "string" ],
  "args": "string",
  "codeId": "string",
  "conf": {
    "{customized property}": "string"
  },
  "entry": {
    "sparkJobEntryType": "string"
    // For remaining properties, see SparkJobEntry objects
  },
  "environmentId": "string",
  "environmentVariables": {
    "{customized property}": "string"
  },
  "files": [ "string" ],
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "jars": [ "string" ],
  "jobType": "Spark",
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "pyFiles": [ "string" ],
  "queueSettings": {
    "jobTier": "string"
  },
  "resources": {
    "instanceType": "string",
    "runtimeVersion": "string"
  }
}

For Sweep, use:

{
  "earlyTermination": {
    "delayEvaluation": "int",
    "evaluationInterval": "int",
    "policyType": "string"
    // For remaining properties, see EarlyTerminationPolicy objects
  },
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "jobType": "Sweep",
  "limits": {
    "jobLimitsType": "string",
    "maxConcurrentTrials": "int",
    "maxTotalTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "objective": {
    "goal": "string",
    "primaryMetric": "string"
  },
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "queueSettings": {
    "jobTier": "string"
  },
  "samplingAlgorithm": {
    "samplingAlgorithmType": "string"
    // For remaining properties, see SamplingAlgorithm objects
  },
  "searchSpace": {},
  "trial": {
    "codeId": "string",
    "command": "string",
    "distribution": {
      "distributionType": "string"
      // For remaining properties, see DistributionConfiguration objects
    },
    "environmentId": "string",
    "environmentVariables": {
      "{customized property}": "string"
    },
    "resources": {
      "dockerArgs": "string",
      "instanceCount": "int",
      "instanceType": "string",
      "properties": {
        "{customized property}": {}
      },
      "shmSize": "string"
    }
  }
}

AutoMLVertical objects

Set the taskType property to specify the type of object.

For Classification, use:

{
  "cvSplitColumnNames": [ "string" ],
  "featurizationSettings": {
    "blockedTransformers": [ "string" ],
    "columnNameAndTypes": {
      "{customized property}": "string"
    },
    "datasetLanguage": "string",
    "enableDnnFeaturization": "bool",
    "mode": "string",
    "transformerParams": {
      "{customized property}": [
        {
          "fields": [ "string" ],
          "parameters": {}
        }
      ]
    }
  },
  "limitSettings": {
    "enableEarlyTermination": "bool",
    "exitScore": "int",
    "maxConcurrentTrials": "int",
    "maxCoresPerTrial": "int",
    "maxTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "nCrossValidations": {
    "mode": "string"
    // For remaining properties, see NCrossValidations objects
  },
  "positiveLabel": "string",
  "primaryMetric": "string",
  "taskType": "Classification",
  "testData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "testDataSize": "int",
  "trainingSettings": {
    "allowedTrainingAlgorithms": [ "string" ],
    "blockedTrainingAlgorithms": [ "string" ],
    "enableDnnTraining": "bool",
    "enableModelExplainability": "bool",
    "enableOnnxCompatibleModels": "bool",
    "enableStackEnsemble": "bool",
    "enableVoteEnsemble": "bool",
    "ensembleModelDownloadTimeout": "string",
    "stackEnsembleSettings": {
      "stackMetaLearnerKWargs": {},
      "stackMetaLearnerTrainPercentage": "int",
      "stackMetaLearnerType": "string"
    }
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int",
  "weightColumnName": "string"
}

For Forecasting, use:

{
  "cvSplitColumnNames": [ "string" ],
  "featurizationSettings": {
    "blockedTransformers": [ "string" ],
    "columnNameAndTypes": {
      "{customized property}": "string"
    },
    "datasetLanguage": "string",
    "enableDnnFeaturization": "bool",
    "mode": "string",
    "transformerParams": {
      "{customized property}": [
        {
          "fields": [ "string" ],
          "parameters": {}
        }
      ]
    }
  },
  "forecastingSettings": {
    "countryOrRegionForHolidays": "string",
    "cvStepSize": "int",
    "featureLags": "string",
    "forecastHorizon": {
      "mode": "string"
      // For remaining properties, see ForecastHorizon objects
    },
    "frequency": "string",
    "seasonality": {
      "mode": "string"
      // For remaining properties, see Seasonality objects
    },
    "shortSeriesHandlingConfig": "string",
    "targetAggregateFunction": "string",
    "targetLags": {
      "mode": "string"
      // For remaining properties, see TargetLags objects
    },
    "targetRollingWindowSize": {
      "mode": "string"
      // For remaining properties, see TargetRollingWindowSize objects
    },
    "timeColumnName": "string",
    "timeSeriesIdColumnNames": [ "string" ],
    "useStl": "string"
  },
  "limitSettings": {
    "enableEarlyTermination": "bool",
    "exitScore": "int",
    "maxConcurrentTrials": "int",
    "maxCoresPerTrial": "int",
    "maxTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "nCrossValidations": {
    "mode": "string"
    // For remaining properties, see NCrossValidations objects
  },
  "primaryMetric": "string",
  "taskType": "Forecasting",
  "testData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "testDataSize": "int",
  "trainingSettings": {
    "allowedTrainingAlgorithms": [ "string" ],
    "blockedTrainingAlgorithms": [ "string" ],
    "enableDnnTraining": "bool",
    "enableModelExplainability": "bool",
    "enableOnnxCompatibleModels": "bool",
    "enableStackEnsemble": "bool",
    "enableVoteEnsemble": "bool",
    "ensembleModelDownloadTimeout": "string",
    "stackEnsembleSettings": {
      "stackMetaLearnerKWargs": {},
      "stackMetaLearnerTrainPercentage": "int",
      "stackMetaLearnerType": "string"
    }
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int",
  "weightColumnName": "string"
}

For ImageClassification, use:

{
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "momentum": "int",
    "nesterov": "bool",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "trainingBatchSize": "int",
    "trainingCropSize": "int",
    "validationBatchSize": "int",
    "validationCropSize": "int",
    "validationResizeSize": "int",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int",
    "weightedLoss": "int"
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "momentum": "string",
      "nesterov": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "trainingBatchSize": "string",
      "trainingCropSize": "string",
      "validationBatchSize": "string",
      "validationCropSize": "string",
      "validationResizeSize": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string",
      "weightedLoss": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "taskType": "ImageClassification",
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"
}

For ImageClassificationMultilabel, use:

{
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "momentum": "int",
    "nesterov": "bool",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "trainingBatchSize": "int",
    "trainingCropSize": "int",
    "validationBatchSize": "int",
    "validationCropSize": "int",
    "validationResizeSize": "int",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int",
    "weightedLoss": "int"
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "momentum": "string",
      "nesterov": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "trainingBatchSize": "string",
      "trainingCropSize": "string",
      "validationBatchSize": "string",
      "validationCropSize": "string",
      "validationResizeSize": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string",
      "weightedLoss": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "taskType": "ImageClassificationMultilabel",
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"
}

For ImageInstanceSegmentation, use:

{
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "boxDetectionsPerImage": "int",
    "boxScoreThreshold": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "imageSize": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "maxSize": "int",
    "minSize": "int",
    "modelName": "string",
    "modelSize": "string",
    "momentum": "int",
    "multiScale": "bool",
    "nesterov": "bool",
    "nmsIouThreshold": "int",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "tileGridSize": "string",
    "tileOverlapRatio": "int",
    "tilePredictionsNmsThreshold": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "validationIouThreshold": "int",
    "validationMetricType": "string",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int"
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "boxDetectionsPerImage": "string",
      "boxScoreThreshold": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "imageSize": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "maxSize": "string",
      "minSize": "string",
      "modelName": "string",
      "modelSize": "string",
      "momentum": "string",
      "multiScale": "string",
      "nesterov": "string",
      "nmsIouThreshold": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "tileGridSize": "string",
      "tileOverlapRatio": "string",
      "tilePredictionsNmsThreshold": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "validationIouThreshold": "string",
      "validationMetricType": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "taskType": "ImageInstanceSegmentation",
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"
}

For ImageObjectDetection, use:

{
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "boxDetectionsPerImage": "int",
    "boxScoreThreshold": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "imageSize": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "maxSize": "int",
    "minSize": "int",
    "modelName": "string",
    "modelSize": "string",
    "momentum": "int",
    "multiScale": "bool",
    "nesterov": "bool",
    "nmsIouThreshold": "int",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "tileGridSize": "string",
    "tileOverlapRatio": "int",
    "tilePredictionsNmsThreshold": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "validationIouThreshold": "int",
    "validationMetricType": "string",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int"
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "boxDetectionsPerImage": "string",
      "boxScoreThreshold": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "imageSize": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "maxSize": "string",
      "minSize": "string",
      "modelName": "string",
      "modelSize": "string",
      "momentum": "string",
      "multiScale": "string",
      "nesterov": "string",
      "nmsIouThreshold": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "tileGridSize": "string",
      "tileOverlapRatio": "string",
      "tilePredictionsNmsThreshold": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "validationIouThreshold": "string",
      "validationMetricType": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "taskType": "ImageObjectDetection",
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"
}

For Regression, use:

{
  "cvSplitColumnNames": [ "string" ],
  "featurizationSettings": {
    "blockedTransformers": [ "string" ],
    "columnNameAndTypes": {
      "{customized property}": "string"
    },
    "datasetLanguage": "string",
    "enableDnnFeaturization": "bool",
    "mode": "string",
    "transformerParams": {
      "{customized property}": [
        {
          "fields": [ "string" ],
          "parameters": {}
        }
      ]
    }
  },
  "limitSettings": {
    "enableEarlyTermination": "bool",
    "exitScore": "int",
    "maxConcurrentTrials": "int",
    "maxCoresPerTrial": "int",
    "maxTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "nCrossValidations": {
    "mode": "string"
    // For remaining properties, see NCrossValidations objects
  },
  "primaryMetric": "string",
  "taskType": "Regression",
  "testData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "testDataSize": "int",
  "trainingSettings": {
    "allowedTrainingAlgorithms": [ "string" ],
    "blockedTrainingAlgorithms": [ "string" ],
    "enableDnnTraining": "bool",
    "enableModelExplainability": "bool",
    "enableOnnxCompatibleModels": "bool",
    "enableStackEnsemble": "bool",
    "enableVoteEnsemble": "bool",
    "ensembleModelDownloadTimeout": "string",
    "stackEnsembleSettings": {
      "stackMetaLearnerKWargs": {},
      "stackMetaLearnerTrainPercentage": "int",
      "stackMetaLearnerType": "string"
    }
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int",
  "weightColumnName": "string"
}

For TextClassification, use:

{
  "featurizationSettings": {
    "datasetLanguage": "string"
  },
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "primaryMetric": "string",
  "taskType": "TextClassification",
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  }
}

For TextClassificationMultilabel, use:

{
  "featurizationSettings": {
    "datasetLanguage": "string"
  },
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "taskType": "TextClassificationMultilabel",
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  }
}

For TextNER, use:

{
  "featurizationSettings": {
    "datasetLanguage": "string"
  },
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "taskType": "TextNER",
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  }
}

EarlyTerminationPolicy objects

Set the policyType property to specify the type of object.

For Bandit, use:

{
  "policyType": "Bandit",
  "slackAmount": "int",
  "slackFactor": "int"
}

For MedianStopping, use:

{
  "policyType": "MedianStopping"
}

For TruncationSelection, use:

{
  "policyType": "TruncationSelection",
  "truncationPercentage": "int"
}

DataQualityMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

{
  "dataType": "Categorical",
  "metric": "string"
}

For Numerical, use:

{
  "dataType": "Numerical",
  "metric": "string"
}

MonitoringSignalBase objects

Set the signalType property to specify the type of object.

For Custom, use:

{
  "componentId": "string",
  "inputAssets": {
    "{customized property}": {
      "columns": {
        "{customized property}": "string"
      },
      "dataContext": "string",
      "jobInputType": "string",
      "uri": "string",
      "inputDataType": "string"
      // For remaining properties, see MonitoringInputDataBase objects
    }
  },
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "metricThresholds": [
    {
      "metric": "string",
      "threshold": {
        "value": "int"
      }
    }
  ],
  "signalType": "Custom"
}

For DataDrift, use:

{
  "featureDataTypeOverride": {
    "{customized property}": "string"
  },
  "featureImportanceSettings": {
    "mode": "string",
    "targetColumn": "string"
  },
  "features": {
    "filterType": "string"
    // For remaining properties, see MonitoringFeatureFilterBase objects
  },
  "metricThresholds": [ {
    "threshold": {
      "value": "int"
    },
    "dataType": "string"
    // For remaining properties, see DataDriftMetricThresholdBase objects
  } ],
  "productionData": {
    "columns": {
      "{customized property}": "string"
    },
    "dataContext": "string",
    "jobInputType": "string",
    "uri": "string",
    "inputDataType": "string"
    // For remaining properties, see MonitoringInputDataBase objects
  },
  "referenceData": {
    "columns": {
      "{customized property}": "string"
    },
    "dataContext": "string",
    "jobInputType": "string",
    "uri": "string",
    "inputDataType": "string"
    // For remaining properties, see MonitoringInputDataBase objects
  },
  "signalType": "DataDrift"
}

For DataQuality, use:

{
  "featureDataTypeOverride": {
    "{customized property}": "string"
  },
  "featureImportanceSettings": {
    "mode": "string",
    "targetColumn": "string"
  },
  "features": {
    "filterType": "string"
    // For remaining properties, see MonitoringFeatureFilterBase objects
  },
  "metricThresholds": [ {
    "threshold": {
      "value": "int"
    },
    "dataType": "string"
    // For remaining properties, see DataQualityMetricThresholdBase objects
  } ],
  "productionData": {
    "columns": {
      "{customized property}": "string"
    },
    "dataContext": "string",
    "jobInputType": "string",
    "uri": "string",
    "inputDataType": "string"
    // For remaining properties, see MonitoringInputDataBase objects
  },
  "referenceData": {
    "columns": {
      "{customized property}": "string"
    },
    "dataContext": "string",
    "jobInputType": "string",
    "uri": "string",
    "inputDataType": "string"
    // For remaining properties, see MonitoringInputDataBase objects
  },
  "signalType": "DataQuality"
}

For FeatureAttributionDrift, use:

{
  "featureDataTypeOverride": {
    "{customized property}": "string"
  },
  "featureImportanceSettings": {
    "mode": "string",
    "targetColumn": "string"
  },
  "metricThreshold": {
    "metric": "string",
    "threshold": {
      "value": "int"
    }
  },
  "productionData": [ {
    "columns": {
      "{customized property}": "string"
    },
    "dataContext": "string",
    "jobInputType": "string",
    "uri": "string",
    "inputDataType": "string"
    // For remaining properties, see MonitoringInputDataBase objects
  } ],
  "referenceData": {
    "columns": {
      "{customized property}": "string"
    },
    "dataContext": "string",
    "jobInputType": "string",
    "uri": "string",
    "inputDataType": "string"
    // For remaining properties, see MonitoringInputDataBase objects
  },
  "signalType": "FeatureAttributionDrift"
}

For PredictionDrift, use:

{
  "featureDataTypeOverride": {
    "{customized property}": "string"
  },
  "metricThresholds": [ {
    "threshold": {
      "value": "int"
    },
    "dataType": "string"
    // For remaining properties, see PredictionDriftMetricThresholdBase objects
  } ],
  "productionData": {
    "columns": {
      "{customized property}": "string"
    },
    "dataContext": "string",
    "jobInputType": "string",
    "uri": "string",
    "inputDataType": "string"
    // For remaining properties, see MonitoringInputDataBase objects
  },
  "referenceData": {
    "columns": {
      "{customized property}": "string"
    },
    "dataContext": "string",
    "jobInputType": "string",
    "uri": "string",
    "inputDataType": "string"
    // For remaining properties, see MonitoringInputDataBase objects
  },
  "signalType": "PredictionDrift"
}

Seasonality objects

Set the mode property to specify the type of object.

For Auto, use:

{
  "mode": "Auto"
}

For Custom, use:

{
  "mode": "Custom",
  "value": "int"
}

FineTuningVertical objects

Set the modelProvider property to specify the type of object.

For AzureOpenAI, use:

{
  "hyperParameters": {
    "batchSize": "int",
    "learningRateMultiplier": "int",
    "nEpochs": "int"
  },
  "modelProvider": "AzureOpenAI"
}

For Custom, use:

{
  "hyperParameters": {
    "{customized property}": "string"
  },
  "modelProvider": "Custom"
}

IdentityConfiguration objects

Set the identityType property to specify the type of object.

For AMLToken, use:

{
  "identityType": "AMLToken"
}

For Managed, use:

{
  "clientId": "string",
  "identityType": "Managed",
  "objectId": "string",
  "resourceId": "string"
}

For UserIdentity, use:

{
  "identityType": "UserIdentity"
}

TargetRollingWindowSize objects

Set the mode property to specify the type of object.

For Auto, use:

{
  "mode": "Auto"
}

For Custom, use:

{
  "mode": "Custom",
  "value": "int"
}

JobOutput objects

Set the jobOutputType property to specify the type of object.

For custom_model, use:

{
  "jobOutputType": "custom_model",
  "mode": "string",
  "uri": "string"
}

For mlflow_model, use:

{
  "jobOutputType": "mlflow_model",
  "mode": "string",
  "uri": "string"
}

For mltable, use:

{
  "jobOutputType": "mltable",
  "mode": "string",
  "uri": "string"
}

For triton_model, use:

{
  "jobOutputType": "triton_model",
  "mode": "string",
  "uri": "string"
}

For uri_file, use:

{
  "jobOutputType": "uri_file",
  "mode": "string",
  "uri": "string"
}

For uri_folder, use:

{
  "jobOutputType": "uri_folder",
  "mode": "string",
  "uri": "string"
}

NCrossValidations objects

Set the mode property to specify the type of object.

For Auto, use:

{
  "mode": "Auto"
}

For Custom, use:

{
  "mode": "Custom",
  "value": "int"
}

MonitoringInputDataBase objects

Set the inputDataType property to specify the type of object.

For Fixed, use:

{
  "inputDataType": "Fixed"
}

For Rolling, use:

{
  "inputDataType": "Rolling",
  "preprocessingComponentId": "string",
  "windowOffset": "string",
  "windowSize": "string"
}

For Static, use:

{
  "inputDataType": "Static",
  "preprocessingComponentId": "string",
  "windowEnd": "string",
  "windowStart": "string"
}

ForecastHorizon objects

Set the mode property to specify the type of object.

For Auto, use:

{
  "mode": "Auto"
}

For Custom, use:

{
  "mode": "Custom",
  "value": "int"
}

SamplingAlgorithm objects

Set the samplingAlgorithmType property to specify the type of object.

For Bayesian, use:

{
  "samplingAlgorithmType": "Bayesian"
}

For Grid, use:

{
  "samplingAlgorithmType": "Grid"
}

For Random, use:

{
  "rule": "string",
  "samplingAlgorithmType": "Random",
  "seed": "int"
}

MonitoringFeatureFilterBase objects

Set the filterType property to specify the type of object.

For AllFeatures, use:

{
  "filterType": "AllFeatures"
}

For FeatureSubset, use:

{
  "features": [ "string" ],
  "filterType": "FeatureSubset"
}

For TopNByAttribution, use:

{
  "filterType": "TopNByAttribution",
  "top": "int"
}

MonitorComputeConfigurationBase objects

Set the computeType property to specify the type of object.

For ServerlessSpark, use:

{
  "computeIdentity": {
    "computeIdentityType": "string"
    // For remaining properties, see MonitorComputeIdentityBase objects
  },
  "computeType": "ServerlessSpark",
  "instanceType": "string",
  "runtimeVersion": "string"
}

Property values

AllFeatures

Name	Description	Value
filterType	[Required] Specifies the feature filter to leverage when selecting features to calculate metrics over.	'AllFeatures' (required)

AllNodes

Name	Description	Value
nodesValueType	[Required] Type of the Nodes value	'All' (required)

AmlToken

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'AMLToken' (required)

AmlTokenComputeIdentity

Name	Description	Value
computeIdentityType	[Required] Specifies the type of identity to use within the monitoring jobs.	'AmlToken' (required)

AutoForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	'Auto' (required)

AutoMLJob

Name	Description	Value
environmentId	The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.	string
environmentVariables	Environment variables included in the job.	AutoMLJobEnvironmentVariables
jobType	[Required] Specifies the type of job.	'AutoML' (required)
outputs	Mapping of output data bindings used in the job.	AutoMLJobOutputs
queueSettings	Queue settings for the job	QueueSettings
resources	Compute Resource configuration for the job.	JobResourceConfiguration
taskDetails	[Required] This represents scenario which can be one of Tables/NLP/Image	AutoMLVertical (required)

AutoMLJobEnvironmentVariables

Name	Description	Value

AutoMLJobOutputs

Name	Description	Value

AutoMLVertical

Name	Description	Value
logVerbosity	Log verbosity for the job.	'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning'
targetColumnName	Target column name: This is prediction values column. Also known as label column name in context of classification tasks.	string
taskType	Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer.	'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required)
trainingData	[Required] Training data input.	MLTableJobInput (required)

AutoNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	'Auto' (required)

AutoSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	'Auto' (required)

AutoTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	'Auto' (required)

AutoTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	'Auto' (required)

AzureDevOpsWebhook

Name	Description	Value
webhookType	[Required] Specifies the type of service to send a callback	'AzureDevOps' (required)

AzureOpenAiFineTuning

Name	Description	Value
hyperParameters	HyperParameters for fine tuning Azure Open AI model.	AzureOpenAiHyperParameters
modelProvider	[Required] Enum to determine the type of fine tuning.	'AzureOpenAI' (required)

AzureOpenAiHyperParameters

Name	Description	Value
batchSize	Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.	int
learningRateMultiplier	Scaling factor for the learning rate. A smaller learning rate may be useful to avoid over fitting.	int
nEpochs	The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.	int

BanditPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'Bandit' (required)
slackAmount	Absolute distance allowed from the best performing run.	int
slackFactor	Ratio of the allowed distance from the best performing run.	int

BayesianSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Bayesian' (required)

CategoricalDataDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Categorical' (required)
metric	[Required] The categorical data drift metric to calculate.	'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required)

CategoricalDataQualityMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Categorical' (required)
metric	[Required] The categorical data quality metric to calculate.	'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required)

CategoricalPredictionDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Categorical' (required)
metric	[Required] The categorical prediction drift metric to calculate.	'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required)

Classification

Name	Description	Value
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
positiveLabel	Positive label for binary metrics calculation.	string
primaryMetric	Primary metric for the task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
taskType	[Required] Task type for AutoMLJob.	'Classification' (required)
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ClassificationTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

ClassificationTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for classification task.	String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier'
blockedTrainingAlgorithms	Blocked models for classification task.	String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings

ColumnTransformer

Name	Description	Value
fields	Fields to apply transformer logic on.	string[]
parameters	Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.	any

CommandJob

Name	Description	Value
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
environmentVariables	Environment variables included in the job.	CommandJobEnvironmentVariables
inputs	Mapping of input data bindings used in the job.	CommandJobInputs
jobType	[Required] Specifies the type of job.	'Command' (required)
limits	Command Job limit.	CommandJobLimits
outputs	Mapping of output data bindings used in the job.	CommandJobOutputs
queueSettings	Queue settings for the job	QueueSettings
resources	Compute Resource configuration for the job.	JobResourceConfiguration

CommandJobEnvironmentVariables

Name	Description	Value

CommandJobInputs

Name	Description	Value

CommandJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	'Command' 'Sweep' (required)
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string

CommandJobOutputs

Name	Description	Value

CreateMonitorAction

Name	Description	Value
actionType	[Required] Specifies the action type of the schedule	'CreateMonitor' (required)
monitorDefinition	[Required] Defines the monitor.	MonitorDefinition (required)

CronTrigger

Name	Description	Value
expression	[Required] Specifies cron expression of schedule. The expression should follow NCronTab format.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
triggerType	[Required]	'Cron' (required)

CustomForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	'Custom' (required)
value	[Required] Forecast horizon value.	int (required)

CustomMetricThreshold

Name	Description	Value
metric	[Required] The user-defined metric to calculate.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

CustomModelFineTuning

Name	Description	Value
hyperParameters	HyperParameters for fine tuning custom model.	CustomModelFineTuningHyperParameters
modelProvider	[Required] Enum to determine the type of fine tuning.	'Custom' (required)

CustomModelFineTuningHyperParameters

Name	Description	Value

CustomModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'custom_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

CustomModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'custom_model' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

CustomMonitoringSignal

Name	Description	Value
componentId	[Required] Reference to the component asset used to calculate the custom metrics.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
inputAssets	Monitoring assets to take as input. Key is the component input port name, value is the data asset.	CustomMonitoringSignalInputAssets
inputs	Extra component parameters to take as input. Key is the component literal input port name, value is the parameter value.	CustomMonitoringSignalInputs
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	CustomMetricThreshold[] (required)
signalType	[Required] Specifies the type of signal to monitor.	'Custom' (required)

CustomMonitoringSignalInputAssets

Name	Description	Value

CustomMonitoringSignalInputs

Name	Description	Value

CustomNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	'Custom' (required)
value	[Required] N-Cross validations value.	int (required)

CustomSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	'Custom' (required)
value	[Required] Seasonality value.	int (required)

CustomTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	'Custom' (required)
values	[Required] Set target lags values.	int[] (required)

CustomTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	'Custom' (required)
value	[Required] TargetRollingWindowSize value.	int (required)

DataDriftMetricThresholdBase

Name	Description	Value
dataType	Set to 'Categorical' for type CategoricalDataDriftMetricThreshold. Set to 'Numerical' for type NumericalDataDriftMetricThreshold.	'Categorical' 'Numerical' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

DataDriftMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	DataDriftMonitoringSignalFeatureDataTypeOverride
featureImportanceSettings	The settings for computing feature importance.	FeatureImportanceSettings
features	The feature filter which identifies which feature to calculate drift over.	MonitoringFeatureFilterBase
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	DataDriftMetricThresholdBase[] (required)
productionData	[Required] The data which drift will be calculated for.	MonitoringInputDataBase (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'DataDrift' (required)

DataDriftMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

DataQualityMetricThresholdBase

Name	Description	Value
dataType	Set to 'Categorical' for type CategoricalDataQualityMetricThreshold. Set to 'Numerical' for type NumericalDataQualityMetricThreshold.	'Categorical' 'Numerical' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

DataQualityMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	DataQualityMonitoringSignalFeatureDataTypeOverride
featureImportanceSettings	The settings for computing feature importance.	FeatureImportanceSettings
features	The features to calculate drift over.	MonitoringFeatureFilterBase
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	DataQualityMetricThresholdBase[] (required)
productionData	[Required] The data produced by the production service which drift will be calculated for.	MonitoringInputDataBase (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'DataQuality' (required)

DataQualityMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

DistributionConfiguration

Name	Description	Value
distributionType	Set to 'Mpi' for type Mpi. Set to 'PyTorch' for type PyTorch. Set to 'TensorFlow' for type TensorFlow.	'Mpi' 'PyTorch' 'TensorFlow' (required)

EarlyTerminationPolicy

Name	Description	Value
delayEvaluation	Number of intervals by which to delay the first evaluation.	int
evaluationInterval	Interval (number of runs) between policy evaluations.	int
policyType	Set to 'Bandit' for type BanditPolicy. Set to 'MedianStopping' for type MedianStoppingPolicy. Set to 'TruncationSelection' for type TruncationSelectionPolicy.	'Bandit' 'MedianStopping' 'TruncationSelection' (required)

EndpointScheduleAction

Name	Description	Value
actionType	[Required] Specifies the action type of the schedule	'InvokeBatchEndpoint' (required)
endpointInvocationDefinition	[Required] Defines Schedule action definition details. <see href="TBD" />	any (required)

FeatureAttributionDriftMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride
featureImportanceSettings	[Required] The settings for computing feature importance.	FeatureImportanceSettings (required)
metricThreshold	[Required] A list of metrics to calculate and their associated thresholds.	FeatureAttributionMetricThreshold (required)
productionData	[Required] The data which drift will be calculated for.	MonitoringInputDataBase[] (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'FeatureAttributionDrift' (required)

FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

FeatureAttributionMetricThreshold

Name	Description	Value
metric	[Required] The feature attribution metric to calculate.	'NormalizedDiscountedCumulativeGain' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

FeatureImportanceSettings

Name	Description	Value
mode	The mode of operation for computing feature importance.	'Disabled' 'Enabled'
targetColumn	The name of the target column within the input data asset.	string

FeatureSubset

Name	Description	Value
features	[Required] The list of features to include.	string[] (required)
filterType	[Required] Specifies the feature filter to leverage when selecting features to calculate metrics over.	'FeatureSubset' (required)

FineTuningJob

Name	Description	Value
fineTuningDetails	[Required]	FineTuningVertical (required)
jobType	[Required] Specifies the type of job.	'FineTuning' (required)
outputs	[Required]	FineTuningJobOutputs (required)
queueSettings	Queue settings for the job	QueueSettings
resources	Instance types and other resources for the job	JobResources

FineTuningJobOutputs

Name	Description	Value

FineTuningVertical

Name	Description	Value
model	[Required] Input model for fine tuning.	MLFlowModelJobInput (required)
modelProvider	Set to 'AzureOpenAI' for type AzureOpenAiFineTuning. Set to 'Custom' for type CustomModelFineTuning.	'AzureOpenAI' 'Custom' (required)
taskType	[Required] Fine tuning task type.	'ChatCompletion' 'ImageClassification' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'QuestionAnswering' 'TextClassification' 'TextCompletion' 'TextSummarization' 'TextTranslation' 'TokenClassification' 'VideoMultiObjectTracking' (required)
trainingData	[Required] Training data for fine tuning.	JobInput (required)
validationData	Validation data for fine tuning.	JobInput

FixedInputData

Name	Description	Value
inputDataType	[Required] Specifies the type of signal to monitor.	'Fixed' (required)

ForecastHorizon

Name	Description	Value
mode	Set to 'Auto' for type AutoForecastHorizon. Set to 'Custom' for type CustomForecastHorizon.	'Auto' 'Custom' (required)

Forecasting

Name	Description	Value
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
forecastingSettings	Forecasting task specific inputs.	ForecastingSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for forecasting task.	'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation'
taskType	[Required] Task type for AutoMLJob.	'Forecasting' (required)
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ForecastingTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

ForecastingSettings

Name	Description	Value
countryOrRegionForHolidays	Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.	string
cvStepSize	Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.	int
featureLags	Flag for generating lags for the numeric features with 'auto' or null.	'Auto' 'None'
forecastHorizon	The desired maximum forecast horizon in units of time-series frequency.	ForecastHorizon
frequency	When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.	string
seasonality	Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.	Seasonality
shortSeriesHandlingConfig	The parameter defining how if AutoML should handle short time series.	'Auto' 'Drop' 'None' 'Pad'
targetAggregateFunction	The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".	'Max' 'Mean' 'Min' 'None' 'Sum'
targetLags	The number of past periods to lag from the target column.	TargetLags
targetRollingWindowSize	The number of past periods used to create a rolling window average of the target column.	TargetRollingWindowSize
timeColumnName	The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.	string
timeSeriesIdColumnNames	The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.	string[]
useStl	Configure STL Decomposition of the time-series target column.	'None' 'Season' 'SeasonTrend'

ForecastingTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for forecasting task.	String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor'
blockedTrainingAlgorithms	Blocked models for forecasting task.	String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings

GridSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Grid' (required)

IdentityConfiguration

Name	Description	Value
identityType	Set to 'AMLToken' for type AmlToken. Set to 'Managed' for type ManagedIdentity. Set to 'UserIdentity' for type UserIdentity.	'AMLToken' 'Managed' 'UserIdentity' (required)

ImageClassification

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageClassification' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageClassificationMultilabel

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'IOU' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageClassificationMultilabel' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageInstanceSegmentation

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	'MeanAveragePrecision'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageInstanceSegmentation' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum number of concurrent AutoML iterations.	int
maxTrials	Maximum number of AutoML iterations.	int
timeout	AutoML job timeout.	string

ImageModelDistributionSettingsClassification

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	string
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	string

ImageModelDistributionSettingsObjectDetection

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	string
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	string
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	string
validationMetricType	Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string

ImageModelSettingsClassification

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	'None' 'Step' 'WarmupCosine'
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	'Adam' 'Adamw' 'None' 'Sgd'
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	int
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	int
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	int

ImageModelSettingsObjectDetection

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	int
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	'None' 'Step' 'WarmupCosine'
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	'ExtraLarge' 'Large' 'Medium' 'None' 'Small'
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	bool
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].	int
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	'Adam' 'Adamw' 'None' 'Sgd'
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	int
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	int
validationMetricType	Metric computation method to use for validation metrics.	'Coco' 'CocoVoc' 'None' 'Voc'
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int

ImageObjectDetection

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	'MeanAveragePrecision'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageObjectDetection' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of the hyperparameter sampling algorithms.	'Bayesian' 'Grid' 'Random' (required)

JobBaseProperties

Name	Description	Value
componentId	ARM resource ID of the component resource.	string
computeId	ARM resource ID of the compute resource.	string
description	The asset description text.	string
displayName	Display name of job.	string
experimentName	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.	string
identity	Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.	IdentityConfiguration
isArchived	Is the asset archived?	bool
jobType	Set to 'AutoML' for type AutoMLJob. Set to 'Command' for type CommandJob. Set to 'FineTuning' for type FineTuningJob. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob.	'AutoML' 'Command' 'FineTuning' 'Pipeline' 'Spark' 'Sweep' (required)
notificationSetting	Notification setting for the job	NotificationSetting
properties	The asset property dictionary.	ResourceBaseProperties
services	List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.	JobBaseServices
tags	Tag dictionary. Tags can be added, removed, and updated.	ResourceBaseTags

JobBaseServices

Name	Description	Value

JobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	Set to 'custom_model' for type CustomModelJobInput. Set to 'literal' for type LiteralJobInput. Set to 'mlflow_model' for type MLFlowModelJobInput. Set to 'mltable' for type MLTableJobInput. Set to 'triton_model' for type TritonModelJobInput. Set to 'uri_file' for type UriFileJobInput. Set to 'uri_folder' for type UriFolderJobInput.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)

JobOutput

Name	Description	Value
description	Description for the output.	string
jobOutputType	Set to 'custom_model' for type CustomModelJobOutput. Set to 'mlflow_model' for type MLFlowModelJobOutput. Set to 'mltable' for type MLTableJobOutput. Set to 'triton_model' for type TritonModelJobOutput. Set to 'uri_file' for type UriFileJobOutput. Set to 'uri_folder' for type UriFolderJobOutput.	'custom_model' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)

JobResourceConfiguration

Name	Description	Value
dockerArgs	Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.	string
instanceCount	Optional number of instances or nodes used by the compute target.	int
instanceType	Optional type of VM used as supported by the compute target.	string
properties	Additional properties bag.	ResourceConfigurationProperties
shmSize	Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).	string Constraints: Pattern = \d+[bBkKmMgG]

JobResources

Name	Description	Value
instanceTypes	List of instance types to choose from.	string[]

JobScheduleAction

Name	Description	Value
actionType	[Required] Specifies the action type of the schedule	'CreateJob' (required)
jobDefinition	[Required] Defines Schedule action definition details.	JobBaseProperties (required)

JobService

Name	Description	Value
endpoint	Url for endpoint.	string
jobServiceType	Endpoint type.	string
nodes	Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.	Nodes
port	Port for endpoint.	int
properties	Additional properties to set on the endpoint.	JobServiceProperties

JobServiceProperties

Name	Description	Value

LiteralJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'literal' (required)
value	[Required] Literal value for the input.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

ManagedComputeIdentity

Name	Description	Value
computeIdentityType	[Required] Specifies the type of identity to use within the monitoring jobs.	'ManagedIdentity' (required)
identity	The identity which will be leveraged by the monitoring jobs.	ManagedServiceIdentity

ManagedIdentity

Name	Description	Value
clientId	Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
identityType	[Required] Specifies the type of identity framework.	'Managed' (required)
objectId	Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
resourceId	Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.	string

ManagedServiceIdentity

Name	Description	Value
type	Type of managed service identity (where both SystemAssigned and UserAssigned types are allowed).	'None' 'SystemAssigned' 'SystemAssigned,UserAssigned' 'UserAssigned' (required)
userAssignedIdentities	The set of user assigned identities associated with the resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form: '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}. The dictionary values can be empty objects ({}) in requests.	UserAssignedIdentities

MedianStoppingPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'MedianStopping' (required)

Microsoft.MachineLearningServices/workspaces/schedules

Name	Description	Value
apiVersion	The api version	'2024-07-01-preview'
name	The resource name	string Constraints: Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (required)
properties	[Required] Additional attributes of the entity.	ScheduleProperties (required)
type	The resource type	'Microsoft.MachineLearningServices/workspaces/schedules'

MLFlowModelJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLFlowModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'mlflow_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLFlowModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'mlflow_model' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

MLTableJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLTableJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'mltable' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLTableJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'mltable' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

MonitorComputeConfigurationBase

Name	Description	Value
computeType	Set to 'ServerlessSpark' for type MonitorServerlessSparkCompute.	'ServerlessSpark' (required)

MonitorComputeIdentityBase

Name	Description	Value
computeIdentityType	Set to 'AmlToken' for type AmlTokenComputeIdentity. Set to 'ManagedIdentity' for type ManagedComputeIdentity.	'AmlToken' 'ManagedIdentity' (required)

MonitorDefinition

Name	Description	Value
alertNotificationSettings	The monitor's notification settings.	MonitorNotificationSettings
computeConfiguration	[Required] The ARM resource ID of the compute resource to run the monitoring job on.	MonitorComputeConfigurationBase (required)
monitoringTarget	The entities targeted by the monitor.	MonitoringTarget
signals	[Required] The signals to monitor.	MonitorDefinitionSignals (required)

MonitorDefinitionSignals

Name	Description	Value

MonitorEmailNotificationSettings

Name	Description	Value
emails	The email recipient list which has a limitation of 499 characters in total.	string[]

MonitoringFeatureFilterBase

Name	Description	Value
filterType	Set to 'AllFeatures' for type AllFeatures. Set to 'FeatureSubset' for type FeatureSubset. Set to 'TopNByAttribution' for type TopNFeaturesByAttribution.	'AllFeatures' 'FeatureSubset' 'TopNByAttribution' (required)

MonitoringInputDataBase

Name	Description	Value
columns	Mapping of column names to special uses.	MonitoringInputDataBaseColumns
dataContext	The context metadata of the data source.	string
inputDataType	Set to 'Fixed' for type FixedInputData. Set to 'Rolling' for type RollingInputData. Set to 'Static' for type StaticInputData.	'Fixed' 'Rolling' 'Static' (required)
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MonitoringInputDataBaseColumns

Name	Description	Value

MonitoringSignalBase

Name	Description	Value
notificationTypes	The current notification mode for this signal.	String array containing any of: 'AmlNotification'
properties	Property dictionary. Properties can be added, but not removed or altered.	MonitoringSignalBaseProperties
signalType	Set to 'Custom' for type CustomMonitoringSignal. Set to 'DataDrift' for type DataDriftMonitoringSignal. Set to 'DataQuality' for type DataQualityMonitoringSignal. Set to 'FeatureAttributionDrift' for type FeatureAttributionDriftMonitoringSignal. Set to 'PredictionDrift' for type PredictionDriftMonitoringSignal.	'Custom' 'DataDrift' 'DataQuality' 'FeatureAttributionDrift' 'PredictionDrift' (required)

MonitoringSignalBaseProperties

Name	Description	Value

MonitoringTarget

Name	Description	Value
deploymentId	Reference to the deployment asset targeted by this monitor.	string
modelId	Reference to the model asset targeted by this monitor.	string
taskType	[Required] The machine learning task type of the monitored model.	'Classification' 'Regression' (required)

MonitoringThreshold

Name	Description	Value
value	The threshold value. If null, the set default is dependent on the metric type.	int

MonitorNotificationSettings

Name	Description	Value
emailNotificationSettings	The AML notification email settings.	MonitorEmailNotificationSettings

MonitorServerlessSparkCompute

Name	Description	Value
computeIdentity	[Required] The identity scheme leveraged to by the spark jobs running on serverless Spark.	MonitorComputeIdentityBase (required)
computeType	[Required] Specifies the type of signal to monitor.	'ServerlessSpark' (required)
instanceType	[Required] The instance type running the Spark job.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
runtimeVersion	[Required] The Spark runtime version.	string Constraints: Min length = 1 Pattern = ^[0-9]+\.[0-9]+$ (required)

Mpi

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'Mpi' (required)
processCountPerInstance	Number of processes per MPI node.	int

NCrossValidations

Name	Description	Value
mode	Set to 'Auto' for type AutoNCrossValidations. Set to 'Custom' for type CustomNCrossValidations.	'Auto' 'Custom' (required)

NlpVerticalFeaturizationSettings

Name	Description	Value
datasetLanguage	Dataset language, useful for the text data.	string

NlpVerticalLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum Concurrent AutoML iterations.	int
maxTrials	Number of AutoML iterations.	int
timeout	AutoML job timeout.	string

Nodes

Name	Description	Value
nodesValueType	Set to 'All' for type AllNodes.	'All' (required)

NotificationSetting

Name	Description	Value
emailOn	Send email notification to user on specified notification type	String array containing any of: 'JobCancelled' 'JobCompleted' 'JobFailed'
emails	This is the email recipient list which has a limitation of 499 characters in total concat with comma separator	string[]
webhooks	Send webhook callback to a service. Key is a user-provided name for the webhook.	NotificationSettingWebhooks

NotificationSettingWebhooks

Name	Description	Value

NumericalDataDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Numerical' (required)
metric	[Required] The numerical data drift metric to calculate.	'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required)

NumericalDataQualityMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Numerical' (required)
metric	[Required] The numerical data quality metric to calculate.	'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required)

NumericalPredictionDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Numerical' (required)
metric	[Required] The numerical prediction drift metric to calculate.	'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required)

Objective

Name	Description	Value
goal	[Required] Defines supported metric goals for hyperparameter tuning	'Maximize' 'Minimize' (required)
primaryMetric	[Required] Name of the metric to optimize.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

PipelineJob

Name	Description	Value
inputs	Inputs for the pipeline job.	PipelineJobInputs
jobs	Jobs construct the Pipeline Job.	PipelineJobJobs
jobType	[Required] Specifies the type of job.	'Pipeline' (required)
outputs	Outputs for the pipeline job	PipelineJobOutputs
settings	Pipeline settings, for things like ContinueRunOnStepFailure etc.	any
sourceJobId	ARM resource ID of source job.	string

PipelineJobInputs

Name	Description	Value

PipelineJobJobs

Name	Description	Value

PipelineJobOutputs

Name	Description	Value

PredictionDriftMetricThresholdBase

Name	Description	Value
dataType	Set to 'Categorical' for type CategoricalPredictionDriftMetricThreshold. Set to 'Numerical' for type NumericalPredictionDriftMetricThreshold.	'Categorical' 'Numerical' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

PredictionDriftMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	PredictionDriftMonitoringSignalFeatureDataTypeOverride
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	PredictionDriftMetricThresholdBase[] (required)
productionData	[Required] The data which drift will be calculated for.	MonitoringInputDataBase (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'PredictionDrift' (required)

PredictionDriftMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

PyTorch

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'PyTorch' (required)
processCountPerInstance	Number of processes per node.	int

QueueSettings

Name	Description	Value
jobTier	Controls the compute job tier	'Basic' 'Null' 'Premium' 'Spot' 'Standard'

RandomSamplingAlgorithm

Name	Description	Value
rule	The specific type of random algorithm	'Random' 'Sobol'
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Random' (required)
seed	An optional integer to use as the seed for random number generation	int

RecurrenceSchedule

Name	Description	Value
hours	[Required] List of hours for the schedule.	int[] (required)
minutes	[Required] List of minutes for the schedule.	int[] (required)
monthDays	List of month days for the schedule	int[]
weekDays	List of days for the schedule.	String array containing any of: 'Friday' 'Monday' 'Saturday' 'Sunday' 'Thursday' 'Tuesday' 'Wednesday'

RecurrenceTrigger

Name	Description	Value
frequency	[Required] The frequency to trigger schedule.	'Day' 'Hour' 'Minute' 'Month' 'Week' (required)
interval	[Required] Specifies schedule interval in conjunction with frequency	int (required)
schedule	The recurrence schedule.	RecurrenceSchedule
triggerType	[Required]	'Recurrence' (required)

Regression

Name	Description	Value
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for regression task.	'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation'
taskType	[Required] Task type for AutoMLJob.	'Regression' (required)
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	RegressionTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

RegressionTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for regression task.	String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor'
blockedTrainingAlgorithms	Blocked models for regression task.	String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings

ResourceBaseProperties

Name	Description	Value

ResourceBaseProperties

Name	Description	Value

ResourceBaseTags

Name	Description	Value

ResourceBaseTags

Name	Description	Value

ResourceConfigurationProperties

Name	Description	Value

RollingInputData

Name	Description	Value
inputDataType	[Required] Specifies the type of signal to monitor.	'Rolling' (required)
preprocessingComponentId	Reference to the component asset used to preprocess the data.	string
windowOffset	[Required] The time offset between the end of the data window and the monitor's current run time.	string (required)
windowSize	[Required] The size of the rolling data window.	string (required)

SamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	Set to 'Bayesian' for type BayesianSamplingAlgorithm. Set to 'Grid' for type GridSamplingAlgorithm. Set to 'Random' for type RandomSamplingAlgorithm.	'Bayesian' 'Grid' 'Random' (required)

ScheduleActionBase

Name	Description	Value
actionType	Set to 'CreateJob' for type JobScheduleAction. Set to 'CreateMonitor' for type CreateMonitorAction. Set to 'InvokeBatchEndpoint' for type EndpointScheduleAction.	'CreateJob' 'CreateMonitor' 'InvokeBatchEndpoint' (required)

ScheduleProperties

Name	Description	Value
action	[Required] Specifies the action of the schedule	ScheduleActionBase (required)
description	The asset description text.	string
displayName	Display name of schedule.	string
isEnabled	Is the schedule enabled?	bool
properties	The asset property dictionary.	ResourceBaseProperties
tags	Tag dictionary. Tags can be added, removed, and updated.	ResourceBaseTags
trigger	[Required] Specifies the trigger details	TriggerBase (required)

Seasonality

Name	Description	Value
mode	Set to 'Auto' for type AutoSeasonality. Set to 'Custom' for type CustomSeasonality.	'Auto' 'Custom' (required)

SparkJob

Name	Description	Value
archives	Archive files used in the job.	string[]
args	Arguments for the job.	string
codeId	[Required] arm-id of the code asset.	string (required)
conf	Spark configured properties.	SparkJobConf
entry	[Required] The entry to execute on startup of the job.	SparkJobEntry (required)
environmentId	The ARM resource ID of the Environment specification for the job.	string
environmentVariables	Environment variables included in the job.	SparkJobEnvironmentVariables
files	Files used in the job.	string[]
inputs	Mapping of input data bindings used in the job.	SparkJobInputs
jars	Jar files used in the job.	string[]
jobType	[Required] Specifies the type of job.	'Spark' (required)
outputs	Mapping of output data bindings used in the job.	SparkJobOutputs
pyFiles	Python files used in the job.	string[]
queueSettings	Queue settings for the job	QueueSettings
resources	Compute Resource configuration for the job.	SparkResourceConfiguration

SparkJobConf

Name	Description	Value

SparkJobEntry

Name	Description	Value
sparkJobEntryType	Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry.	'SparkJobPythonEntry' 'SparkJobScalaEntry' (required)

SparkJobEnvironmentVariables

Name	Description	Value

SparkJobInputs

Name	Description	Value

SparkJobOutputs

Name	Description	Value

SparkJobPythonEntry

Name	Description	Value
file	[Required] Relative python file path for job entry point.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
sparkJobEntryType	[Required] Type of the job's entry point.	'SparkJobPythonEntry' (required)

SparkJobScalaEntry

Name	Description	Value
className	[Required] Scala class name used as entry point.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
sparkJobEntryType	[Required] Type of the job's entry point.	'SparkJobScalaEntry' (required)

SparkResourceConfiguration

Name	Description	Value
instanceType	Optional type of VM used as supported by the compute target.	string
runtimeVersion	Version of spark runtime used for the job.	string

StackEnsembleSettings

Name	Description	Value
stackMetaLearnerKWargs	Optional parameters to pass to the initializer of the meta-learner.	any
stackMetaLearnerTrainPercentage	Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.	int
stackMetaLearnerType	The meta-learner is a model trained on the output of the individual heterogeneous models.	'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None'

StaticInputData

Name	Description	Value
inputDataType	[Required] Specifies the type of signal to monitor.	'Static' (required)
preprocessingComponentId	Reference to the component asset used to preprocess the data.	string
windowEnd	[Required] The end date of the data window.	string (required)
windowStart	[Required] The start date of the data window.	string (required)

SweepJob

Name	Description	Value
earlyTermination	Early termination policies enable canceling poor-performing runs before they complete	EarlyTerminationPolicy
inputs	Mapping of input data bindings used in the job.	SweepJobInputs
jobType	[Required] Specifies the type of job.	'Sweep' (required)
limits	Sweep Job limit.	SweepJobLimits
objective	[Required] Optimization objective.	Objective (required)
outputs	Mapping of output data bindings used in the job.	SweepJobOutputs
queueSettings	Queue settings for the job	QueueSettings
samplingAlgorithm	[Required] The hyperparameter sampling algorithm	SamplingAlgorithm (required)
searchSpace	[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter	any (required)
trial	[Required] Trial component definition.	TrialComponent (required)

SweepJobInputs

Name	Description	Value

SweepJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	'Command' 'Sweep' (required)
maxConcurrentTrials	Sweep Job max concurrent trials.	int
maxTotalTrials	Sweep Job max total trials.	int
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string
trialTimeout	Sweep Job Trial timeout value.	string

SweepJobOutputs

Name	Description	Value

TableVerticalFeaturizationSettings

Name	Description	Value
blockedTransformers	These transformers shall not be used in featurization.	String array containing any of: 'CatTargetEncoder' 'CountVectorizer' 'HashOneHotEncoder' 'LabelEncoder' 'NaiveBayes' 'OneHotEncoder' 'TextTargetEncoder' 'TfIdf' 'WoETargetEncoder' 'WordEmbedding'
columnNameAndTypes	Dictionary of column name and its type (int, float, string, datetime etc).	TableVerticalFeaturizationSettingsColumnNameAndTypes
datasetLanguage	Dataset language, useful for the text data.	string
enableDnnFeaturization	Determines whether to use Dnn based featurizers for data featurization.	bool
mode	Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.	'Auto' 'Custom' 'Off'
transformerParams	User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.	TableVerticalFeaturizationSettingsTransformerParams

TableVerticalFeaturizationSettingsColumnNameAndTypes

Name	Description	Value

TableVerticalFeaturizationSettingsTransformerParams

Name	Description	Value

TableVerticalLimitSettings

Name	Description	Value
enableEarlyTermination	Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.	bool
exitScore	Exit score for the AutoML job.	int
maxConcurrentTrials	Maximum Concurrent iterations.	int
maxCoresPerTrial	Max cores per iteration.	int
maxTrials	Number of iterations.	int
timeout	AutoML job timeout.	string
trialTimeout	Iteration timeout.	string

TargetLags

Name	Description	Value
mode	Set to 'Auto' for type AutoTargetLags. Set to 'Custom' for type CustomTargetLags.	'Auto' 'Custom' (required)

TargetRollingWindowSize

Name	Description	Value
mode	Set to 'Auto' for type AutoTargetRollingWindowSize. Set to 'Custom' for type CustomTargetRollingWindowSize.	'Auto' 'Custom' (required)

TensorFlow

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'TensorFlow' (required)
parameterServerCount	Number of parameter server tasks.	int
workerCount	Number of workers. If not specified, will default to the instance count.	int

TextClassification

Name	Description	Value
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
primaryMetric	Primary metric for Text-Classification task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
taskType	[Required] Task type for AutoMLJob.	'TextClassification' (required)
validationData	Validation data inputs.	MLTableJobInput

TextClassificationMultilabel

Name	Description	Value
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
taskType	[Required] Task type for AutoMLJob.	'TextClassificationMultilabel' (required)
validationData	Validation data inputs.	MLTableJobInput

TextNer

Name	Description	Value
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
taskType	[Required] Task type for AutoMLJob.	'TextNER' (required)
validationData	Validation data inputs.	MLTableJobInput

TopNFeaturesByAttribution

Name	Description	Value
filterType	[Required] Specifies the feature filter to leverage when selecting features to calculate metrics over.	'TopNByAttribution' (required)
top	The number of top features to include.	int

TrialComponent

Name	Description	Value
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
environmentVariables	Environment variables included in the job.	TrialComponentEnvironmentVariables
resources	Compute Resource configuration for the job.	JobResourceConfiguration

TrialComponentEnvironmentVariables

Name	Description	Value

TriggerBase

Name	Description	Value
endTime	Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely	string
startTime	Specifies start time of schedule in ISO 8601 format, but without a UTC offset.	string
timeZone	Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones?view=windows-11	string
triggerType	Set to 'Cron' for type CronTrigger. Set to 'Recurrence' for type RecurrenceTrigger.	'Cron' 'Recurrence' (required)

TritonModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'triton_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

TritonModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'triton_model' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

TruncationSelectionPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'TruncationSelection' (required)
truncationPercentage	The percentage of runs to cancel at each evaluation interval.	int

UriFileJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'uri_file' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

UriFileJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'uri_file' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

UriFolderJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

UriFolderJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'uri_folder' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

UserAssignedIdentities

Name	Description	Value

UserAssignedIdentity

Name	Description	Value

UserIdentity

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'UserIdentity' (required)

Webhook

Name	Description	Value
eventType	Send callback on a specified notification event	string
webhookType	Set to 'AzureDevOps' for type AzureDevOpsWebhook.	'AzureDevOps' (required)

Terraform (AzAPI provider) resource definition

The workspaces/schedules resource type can be deployed with operations that target:

• Resource groups

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/schedules resource, add the following Terraform to your template.

resource "azapi_resource" "symbolicname" {
  type = "Microsoft.MachineLearningServices/workspaces/schedules@2024-07-01-preview"
  name = "string"
  body = jsonencode({
    properties = {
      action = {
        actionType = "string"
        // For remaining properties, see ScheduleActionBase objects
      }
      description = "string"
      displayName = "string"
      isEnabled = bool
      properties = {
        {customized property} = "string"
      }
      tags = {
        {customized property} = "string"
      }
      trigger = {
        endTime = "string"
        startTime = "string"
        timeZone = "string"
        triggerType = "string"
        // For remaining properties, see TriggerBase objects
      }
    }
  })
}

TriggerBase objects

Set the triggerType property to specify the type of object.

For Cron, use:

{
  expression = "string"
  triggerType = "Cron"
}

For Recurrence, use:

{
  frequency = "string"
  interval = int
  schedule = {
    hours = [
      int
    ]
    minutes = [
      int
    ]
    monthDays = [
      int
    ]
    weekDays = [
      "string"
    ]
  }
  triggerType = "Recurrence"
}

JobInput objects

Set the jobInputType property to specify the type of object.

For custom_model, use:

{
  jobInputType = "custom_model"
  mode = "string"
  uri = "string"
}

For literal, use:

{
  jobInputType = "literal"
  value = "string"
}

For mlflow_model, use:

{
  jobInputType = "mlflow_model"
  mode = "string"
  uri = "string"
}

For mltable, use:

{
  jobInputType = "mltable"
  mode = "string"
  uri = "string"
}

For triton_model, use:

{
  jobInputType = "triton_model"
  mode = "string"
  uri = "string"
}

For uri_file, use:

{
  jobInputType = "uri_file"
  mode = "string"
  uri = "string"
}

For uri_folder, use:

{
  jobInputType = "uri_folder"
  mode = "string"
  uri = "string"
}

ScheduleActionBase objects

Set the actionType property to specify the type of object.

For CreateJob, use:

{
  actionType = "CreateJob"
  jobDefinition = {
    componentId = "string"
    computeId = "string"
    description = "string"
    displayName = "string"
    experimentName = "string"
    identity = {
      identityType = "string"
      // For remaining properties, see IdentityConfiguration objects
    }
    isArchived = bool
    notificationSetting = {
      emailOn = [
        "string"
      ]
      emails = [
        "string"
      ]
      webhooks = {
        {customized property} = {
          eventType = "string"
          webhookType = "string"
          // For remaining properties, see Webhook objects
        }
      }
    }
    properties = {
      {customized property} = "string"
    }
    services = {
      {customized property} = {
        endpoint = "string"
        jobServiceType = "string"
        nodes = {
          nodesValueType = "string"
          // For remaining properties, see Nodes objects
        }
        port = int
        properties = {
          {customized property} = "string"
        }
      }
    }
    tags = {
      {customized property} = "string"
    }
    jobType = "string"
    // For remaining properties, see JobBaseProperties objects
  }
}

For CreateMonitor, use:

{
  actionType = "CreateMonitor"
  monitorDefinition = {
    alertNotificationSettings = {
      emailNotificationSettings = {
        emails = [
          "string"
        ]
      }
    }
    computeConfiguration = {
      computeType = "string"
      // For remaining properties, see MonitorComputeConfigurationBase objects
    }
    monitoringTarget = {
      deploymentId = "string"
      modelId = "string"
      taskType = "string"
    }
    signals = {
      {customized property} = {
        notificationTypes = [
          "string"
        ]
        properties = {
          {customized property} = "string"
        }
        signalType = "string"
        // For remaining properties, see MonitoringSignalBase objects
      }
    }
  }
}

For InvokeBatchEndpoint, use:

{
  actionType = "InvokeBatchEndpoint"
  endpointInvocationDefinition = ?
}

Nodes objects

Set the nodesValueType property to specify the type of object.

For All, use:

{
  nodesValueType = "All"
}

DistributionConfiguration objects

Set the distributionType property to specify the type of object.

For Mpi, use:

{
  distributionType = "Mpi"
  processCountPerInstance = int
}

For PyTorch, use:

{
  distributionType = "PyTorch"
  processCountPerInstance = int
}

For TensorFlow, use:

{
  distributionType = "TensorFlow"
  parameterServerCount = int
  workerCount = int
}

Webhook objects

Set the webhookType property to specify the type of object.

For AzureDevOps, use:

{
  webhookType = "AzureDevOps"
}

TargetLags objects

Set the mode property to specify the type of object.

For Auto, use:

{
  mode = "Auto"
}

For Custom, use:

{
  mode = "Custom"
  values = [
    int
  ]
}

PredictionDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

{
  dataType = "Categorical"
  metric = "string"
}

For Numerical, use:

{
  dataType = "Numerical"
  metric = "string"
}

DataDriftMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

{
  dataType = "Categorical"
  metric = "string"
}

For Numerical, use:

{
  dataType = "Numerical"
  metric = "string"
}

MonitorComputeIdentityBase objects

Set the computeIdentityType property to specify the type of object.

For AmlToken, use:

{
  computeIdentityType = "AmlToken"
}

For ManagedIdentity, use:

{
  computeIdentityType = "ManagedIdentity"
  identity = {
    type = "string"
    userAssignedIdentities = {
      {customized property} = {
      }
    }
  }
}

SparkJobEntry objects

Set the sparkJobEntryType property to specify the type of object.

For SparkJobPythonEntry, use:

{
  file = "string"
  sparkJobEntryType = "SparkJobPythonEntry"
}

For SparkJobScalaEntry, use:

{
  className = "string"
  sparkJobEntryType = "SparkJobScalaEntry"
}

JobBaseProperties objects

Set the jobType property to specify the type of object.

For AutoML, use:

{
  environmentId = "string"
  environmentVariables = {
    {customized property} = "string"
  }
  jobType = "AutoML"
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings = {
    jobTier = "string"
  }
  resources = {
    dockerArgs = "string"
    instanceCount = int
    instanceType = "string"
    properties = {
      {customized property} = ?
    }
    shmSize = "string"
  }
  taskDetails = {
    logVerbosity = "string"
    targetColumnName = "string"
    trainingData = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    taskType = "string"
    // For remaining properties, see AutoMLVertical objects
  }
}

For Command, use:

{
  codeId = "string"
  command = "string"
  distribution = {
    distributionType = "string"
    // For remaining properties, see DistributionConfiguration objects
  }
  environmentId = "string"
  environmentVariables = {
    {customized property} = "string"
  }
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  jobType = "Command"
  limits = {
    jobLimitsType = "string"
    timeout = "string"
  }
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings = {
    jobTier = "string"
  }
  resources = {
    dockerArgs = "string"
    instanceCount = int
    instanceType = "string"
    properties = {
      {customized property} = ?
    }
    shmSize = "string"
  }
}

For FineTuning, use:

{
  fineTuningDetails = {
    model = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    taskType = "string"
    trainingData = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
    validationData = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
    modelProvider = "string"
    // For remaining properties, see FineTuningVertical objects
  }
  jobType = "FineTuning"
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings = {
    jobTier = "string"
  }
  resources = {
    instanceTypes = [
      "string"
    ]
  }
}

For Pipeline, use:

{
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  jobs = {
    {customized property} = ?
  }
  jobType = "Pipeline"
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  settings = ?
  sourceJobId = "string"
}

For Spark, use:

{
  archives = [
    "string"
  ]
  args = "string"
  codeId = "string"
  conf = {
    {customized property} = "string"
  }
  entry = {
    sparkJobEntryType = "string"
    // For remaining properties, see SparkJobEntry objects
  }
  environmentId = "string"
  environmentVariables = {
    {customized property} = "string"
  }
  files = [
    "string"
  ]
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  jars = [
    "string"
  ]
  jobType = "Spark"
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  pyFiles = [
    "string"
  ]
  queueSettings = {
    jobTier = "string"
  }
  resources = {
    instanceType = "string"
    runtimeVersion = "string"
  }
}

For Sweep, use:

{
  earlyTermination = {
    delayEvaluation = int
    evaluationInterval = int
    policyType = "string"
    // For remaining properties, see EarlyTerminationPolicy objects
  }
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  jobType = "Sweep"
  limits = {
    jobLimitsType = "string"
    maxConcurrentTrials = int
    maxTotalTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  objective = {
    goal = "string"
    primaryMetric = "string"
  }
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  queueSettings = {
    jobTier = "string"
  }
  samplingAlgorithm = {
    samplingAlgorithmType = "string"
    // For remaining properties, see SamplingAlgorithm objects
  }
  searchSpace = ?
  trial = {
    codeId = "string"
    command = "string"
    distribution = {
      distributionType = "string"
      // For remaining properties, see DistributionConfiguration objects
    }
    environmentId = "string"
    environmentVariables = {
      {customized property} = "string"
    }
    resources = {
      dockerArgs = "string"
      instanceCount = int
      instanceType = "string"
      properties = {
        {customized property} = ?
      }
      shmSize = "string"
    }
  }
}

AutoMLVertical objects

Set the taskType property to specify the type of object.

For Classification, use:

{
  cvSplitColumnNames = [
    "string"
  ]
  featurizationSettings = {
    blockedTransformers = [
      "string"
    ]
    columnNameAndTypes = {
      {customized property} = "string"
    }
    datasetLanguage = "string"
    enableDnnFeaturization = bool
    mode = "string"
    transformerParams = {
      {customized property} = [
        {
          fields = [
            "string"
          ]
          parameters = ?
        }
      ]
    }
  }
  limitSettings = {
    enableEarlyTermination = bool
    exitScore = int
    maxConcurrentTrials = int
    maxCoresPerTrial = int
    maxTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  nCrossValidations = {
    mode = "string"
    // For remaining properties, see NCrossValidations objects
  }
  positiveLabel = "string"
  primaryMetric = "string"
  taskType = "Classification"
  testData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  testDataSize = int
  trainingSettings = {
    allowedTrainingAlgorithms = [
      "string"
    ]
    blockedTrainingAlgorithms = [
      "string"
    ]
    enableDnnTraining = bool
    enableModelExplainability = bool
    enableOnnxCompatibleModels = bool
    enableStackEnsemble = bool
    enableVoteEnsemble = bool
    ensembleModelDownloadTimeout = "string"
    stackEnsembleSettings = {
      stackMetaLearnerKWargs = ?
      stackMetaLearnerTrainPercentage = int
      stackMetaLearnerType = "string"
    }
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
  weightColumnName = "string"
}

For Forecasting, use:

{
  cvSplitColumnNames = [
    "string"
  ]
  featurizationSettings = {
    blockedTransformers = [
      "string"
    ]
    columnNameAndTypes = {
      {customized property} = "string"
    }
    datasetLanguage = "string"
    enableDnnFeaturization = bool
    mode = "string"
    transformerParams = {
      {customized property} = [
        {
          fields = [
            "string"
          ]
          parameters = ?
        }
      ]
    }
  }
  forecastingSettings = {
    countryOrRegionForHolidays = "string"
    cvStepSize = int
    featureLags = "string"
    forecastHorizon = {
      mode = "string"
      // For remaining properties, see ForecastHorizon objects
    }
    frequency = "string"
    seasonality = {
      mode = "string"
      // For remaining properties, see Seasonality objects
    }
    shortSeriesHandlingConfig = "string"
    targetAggregateFunction = "string"
    targetLags = {
      mode = "string"
      // For remaining properties, see TargetLags objects
    }
    targetRollingWindowSize = {
      mode = "string"
      // For remaining properties, see TargetRollingWindowSize objects
    }
    timeColumnName = "string"
    timeSeriesIdColumnNames = [
      "string"
    ]
    useStl = "string"
  }
  limitSettings = {
    enableEarlyTermination = bool
    exitScore = int
    maxConcurrentTrials = int
    maxCoresPerTrial = int
    maxTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  nCrossValidations = {
    mode = "string"
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric = "string"
  taskType = "Forecasting"
  testData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  testDataSize = int
  trainingSettings = {
    allowedTrainingAlgorithms = [
      "string"
    ]
    blockedTrainingAlgorithms = [
      "string"
    ]
    enableDnnTraining = bool
    enableModelExplainability = bool
    enableOnnxCompatibleModels = bool
    enableStackEnsemble = bool
    enableVoteEnsemble = bool
    ensembleModelDownloadTimeout = "string"
    stackEnsembleSettings = {
      stackMetaLearnerKWargs = ?
      stackMetaLearnerTrainPercentage = int
      stackMetaLearnerType = "string"
    }
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
  weightColumnName = "string"
}

For ImageClassification, use:

{
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    momentum = int
    nesterov = bool
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    trainingBatchSize = int
    trainingCropSize = int
    validationBatchSize = int
    validationCropSize = int
    validationResizeSize = int
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
    weightedLoss = int
  }
  primaryMetric = "string"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      momentum = "string"
      nesterov = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      trainingBatchSize = "string"
      trainingCropSize = "string"
      validationBatchSize = "string"
      validationCropSize = "string"
      validationResizeSize = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
      weightedLoss = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  taskType = "ImageClassification"
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
}

For ImageClassificationMultilabel, use:

{
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    momentum = int
    nesterov = bool
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    trainingBatchSize = int
    trainingCropSize = int
    validationBatchSize = int
    validationCropSize = int
    validationResizeSize = int
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
    weightedLoss = int
  }
  primaryMetric = "string"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      momentum = "string"
      nesterov = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      trainingBatchSize = "string"
      trainingCropSize = "string"
      validationBatchSize = "string"
      validationCropSize = "string"
      validationResizeSize = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
      weightedLoss = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  taskType = "ImageClassificationMultilabel"
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
}

For ImageInstanceSegmentation, use:

{
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    boxDetectionsPerImage = int
    boxScoreThreshold = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    imageSize = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    maxSize = int
    minSize = int
    modelName = "string"
    modelSize = "string"
    momentum = int
    multiScale = bool
    nesterov = bool
    nmsIouThreshold = int
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    tileGridSize = "string"
    tileOverlapRatio = int
    tilePredictionsNmsThreshold = int
    trainingBatchSize = int
    validationBatchSize = int
    validationIouThreshold = int
    validationMetricType = "string"
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
  }
  primaryMetric = "string"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      boxDetectionsPerImage = "string"
      boxScoreThreshold = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      imageSize = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      maxSize = "string"
      minSize = "string"
      modelName = "string"
      modelSize = "string"
      momentum = "string"
      multiScale = "string"
      nesterov = "string"
      nmsIouThreshold = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      tileGridSize = "string"
      tileOverlapRatio = "string"
      tilePredictionsNmsThreshold = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      validationIouThreshold = "string"
      validationMetricType = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  taskType = "ImageInstanceSegmentation"
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
}

For ImageObjectDetection, use:

{
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    boxDetectionsPerImage = int
    boxScoreThreshold = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    imageSize = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    maxSize = int
    minSize = int
    modelName = "string"
    modelSize = "string"
    momentum = int
    multiScale = bool
    nesterov = bool
    nmsIouThreshold = int
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    tileGridSize = "string"
    tileOverlapRatio = int
    tilePredictionsNmsThreshold = int
    trainingBatchSize = int
    validationBatchSize = int
    validationIouThreshold = int
    validationMetricType = "string"
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
  }
  primaryMetric = "string"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      boxDetectionsPerImage = "string"
      boxScoreThreshold = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      imageSize = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      maxSize = "string"
      minSize = "string"
      modelName = "string"
      modelSize = "string"
      momentum = "string"
      multiScale = "string"
      nesterov = "string"
      nmsIouThreshold = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      tileGridSize = "string"
      tileOverlapRatio = "string"
      tilePredictionsNmsThreshold = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      validationIouThreshold = "string"
      validationMetricType = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  taskType = "ImageObjectDetection"
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
}

For Regression, use:

{
  cvSplitColumnNames = [
    "string"
  ]
  featurizationSettings = {
    blockedTransformers = [
      "string"
    ]
    columnNameAndTypes = {
      {customized property} = "string"
    }
    datasetLanguage = "string"
    enableDnnFeaturization = bool
    mode = "string"
    transformerParams = {
      {customized property} = [
        {
          fields = [
            "string"
          ]
          parameters = ?
        }
      ]
    }
  }
  limitSettings = {
    enableEarlyTermination = bool
    exitScore = int
    maxConcurrentTrials = int
    maxCoresPerTrial = int
    maxTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  nCrossValidations = {
    mode = "string"
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric = "string"
  taskType = "Regression"
  testData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  testDataSize = int
  trainingSettings = {
    allowedTrainingAlgorithms = [
      "string"
    ]
    blockedTrainingAlgorithms = [
      "string"
    ]
    enableDnnTraining = bool
    enableModelExplainability = bool
    enableOnnxCompatibleModels = bool
    enableStackEnsemble = bool
    enableVoteEnsemble = bool
    ensembleModelDownloadTimeout = "string"
    stackEnsembleSettings = {
      stackMetaLearnerKWargs = ?
      stackMetaLearnerTrainPercentage = int
      stackMetaLearnerType = "string"
    }
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
  weightColumnName = "string"
}

For TextClassification, use:

{
  featurizationSettings = {
    datasetLanguage = "string"
  }
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  primaryMetric = "string"
  taskType = "TextClassification"
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
}

For TextClassificationMultilabel, use:

{
  featurizationSettings = {
    datasetLanguage = "string"
  }
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  taskType = "TextClassificationMultilabel"
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
}

For TextNER, use:

{
  featurizationSettings = {
    datasetLanguage = "string"
  }
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  taskType = "TextNER"
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
}

EarlyTerminationPolicy objects

Set the policyType property to specify the type of object.

For Bandit, use:

{
  policyType = "Bandit"
  slackAmount = int
  slackFactor = int
}

For MedianStopping, use:

{
  policyType = "MedianStopping"
}

For TruncationSelection, use:

{
  policyType = "TruncationSelection"
  truncationPercentage = int
}

DataQualityMetricThresholdBase objects

Set the dataType property to specify the type of object.

For Categorical, use:

{
  dataType = "Categorical"
  metric = "string"
}

For Numerical, use:

{
  dataType = "Numerical"
  metric = "string"
}

MonitoringSignalBase objects

Set the signalType property to specify the type of object.

For Custom, use:

{
  componentId = "string"
  inputAssets = {
    {customized property} = {
      columns = {
        {customized property} = "string"
      }
      dataContext = "string"
      jobInputType = "string"
      uri = "string"
      inputDataType = "string"
      // For remaining properties, see MonitoringInputDataBase objects
    }
  }
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  metricThresholds = [
    {
      metric = "string"
      threshold = {
        value = int
      }
    }
  ]
  signalType = "Custom"
}

For DataDrift, use:

{
  featureDataTypeOverride = {
    {customized property} = "string"
  }
  featureImportanceSettings = {
    mode = "string"
    targetColumn = "string"
  }
  features = {
    filterType = "string"
    // For remaining properties, see MonitoringFeatureFilterBase objects
  }
  metricThresholds = [
    {
      threshold = {
        value = int
      }
      dataType = "string"
      // For remaining properties, see DataDriftMetricThresholdBase objects
    }
  ]
  productionData = {
    columns = {
      {customized property} = "string"
    }
    dataContext = "string"
    jobInputType = "string"
    uri = "string"
    inputDataType = "string"
    // For remaining properties, see MonitoringInputDataBase objects
  }
  referenceData = {
    columns = {
      {customized property} = "string"
    }
    dataContext = "string"
    jobInputType = "string"
    uri = "string"
    inputDataType = "string"
    // For remaining properties, see MonitoringInputDataBase objects
  }
  signalType = "DataDrift"
}

For DataQuality, use:

{
  featureDataTypeOverride = {
    {customized property} = "string"
  }
  featureImportanceSettings = {
    mode = "string"
    targetColumn = "string"
  }
  features = {
    filterType = "string"
    // For remaining properties, see MonitoringFeatureFilterBase objects
  }
  metricThresholds = [
    {
      threshold = {
        value = int
      }
      dataType = "string"
      // For remaining properties, see DataQualityMetricThresholdBase objects
    }
  ]
  productionData = {
    columns = {
      {customized property} = "string"
    }
    dataContext = "string"
    jobInputType = "string"
    uri = "string"
    inputDataType = "string"
    // For remaining properties, see MonitoringInputDataBase objects
  }
  referenceData = {
    columns = {
      {customized property} = "string"
    }
    dataContext = "string"
    jobInputType = "string"
    uri = "string"
    inputDataType = "string"
    // For remaining properties, see MonitoringInputDataBase objects
  }
  signalType = "DataQuality"
}

For FeatureAttributionDrift, use:

{
  featureDataTypeOverride = {
    {customized property} = "string"
  }
  featureImportanceSettings = {
    mode = "string"
    targetColumn = "string"
  }
  metricThreshold = {
    metric = "string"
    threshold = {
      value = int
    }
  }
  productionData = [
    {
      columns = {
        {customized property} = "string"
      }
      dataContext = "string"
      jobInputType = "string"
      uri = "string"
      inputDataType = "string"
      // For remaining properties, see MonitoringInputDataBase objects
    }
  ]
  referenceData = {
    columns = {
      {customized property} = "string"
    }
    dataContext = "string"
    jobInputType = "string"
    uri = "string"
    inputDataType = "string"
    // For remaining properties, see MonitoringInputDataBase objects
  }
  signalType = "FeatureAttributionDrift"
}

For PredictionDrift, use:

{
  featureDataTypeOverride = {
    {customized property} = "string"
  }
  metricThresholds = [
    {
      threshold = {
        value = int
      }
      dataType = "string"
      // For remaining properties, see PredictionDriftMetricThresholdBase objects
    }
  ]
  productionData = {
    columns = {
      {customized property} = "string"
    }
    dataContext = "string"
    jobInputType = "string"
    uri = "string"
    inputDataType = "string"
    // For remaining properties, see MonitoringInputDataBase objects
  }
  referenceData = {
    columns = {
      {customized property} = "string"
    }
    dataContext = "string"
    jobInputType = "string"
    uri = "string"
    inputDataType = "string"
    // For remaining properties, see MonitoringInputDataBase objects
  }
  signalType = "PredictionDrift"
}

Seasonality objects

Set the mode property to specify the type of object.

For Auto, use:

{
  mode = "Auto"
}

For Custom, use:

{
  mode = "Custom"
  value = int
}

FineTuningVertical objects

Set the modelProvider property to specify the type of object.

For AzureOpenAI, use:

{
  hyperParameters = {
    batchSize = int
    learningRateMultiplier = int
    nEpochs = int
  }
  modelProvider = "AzureOpenAI"
}

For Custom, use:

{
  hyperParameters = {
    {customized property} = "string"
  }
  modelProvider = "Custom"
}

IdentityConfiguration objects

Set the identityType property to specify the type of object.

For AMLToken, use:

{
  identityType = "AMLToken"
}

For Managed, use:

{
  clientId = "string"
  identityType = "Managed"
  objectId = "string"
  resourceId = "string"
}

For UserIdentity, use:

{
  identityType = "UserIdentity"
}

TargetRollingWindowSize objects

Set the mode property to specify the type of object.

For Auto, use:

{
  mode = "Auto"
}

For Custom, use:

{
  mode = "Custom"
  value = int
}

JobOutput objects

Set the jobOutputType property to specify the type of object.

For custom_model, use:

{
  jobOutputType = "custom_model"
  mode = "string"
  uri = "string"
}

For mlflow_model, use:

{
  jobOutputType = "mlflow_model"
  mode = "string"
  uri = "string"
}

For mltable, use:

{
  jobOutputType = "mltable"
  mode = "string"
  uri = "string"
}

For triton_model, use:

{
  jobOutputType = "triton_model"
  mode = "string"
  uri = "string"
}

For uri_file, use:

{
  jobOutputType = "uri_file"
  mode = "string"
  uri = "string"
}

For uri_folder, use:

{
  jobOutputType = "uri_folder"
  mode = "string"
  uri = "string"
}

NCrossValidations objects

Set the mode property to specify the type of object.

For Auto, use:

{
  mode = "Auto"
}

For Custom, use:

{
  mode = "Custom"
  value = int
}

MonitoringInputDataBase objects

Set the inputDataType property to specify the type of object.

For Fixed, use:

{
  inputDataType = "Fixed"
}

For Rolling, use:

{
  inputDataType = "Rolling"
  preprocessingComponentId = "string"
  windowOffset = "string"
  windowSize = "string"
}

For Static, use:

{
  inputDataType = "Static"
  preprocessingComponentId = "string"
  windowEnd = "string"
  windowStart = "string"
}

ForecastHorizon objects

Set the mode property to specify the type of object.

For Auto, use:

{
  mode = "Auto"
}

For Custom, use:

{
  mode = "Custom"
  value = int
}

SamplingAlgorithm objects

Set the samplingAlgorithmType property to specify the type of object.

For Bayesian, use:

{
  samplingAlgorithmType = "Bayesian"
}

For Grid, use:

{
  samplingAlgorithmType = "Grid"
}

For Random, use:

{
  rule = "string"
  samplingAlgorithmType = "Random"
  seed = int
}

MonitoringFeatureFilterBase objects

Set the filterType property to specify the type of object.

For AllFeatures, use:

{
  filterType = "AllFeatures"
}

For FeatureSubset, use:

{
  features = [
    "string"
  ]
  filterType = "FeatureSubset"
}

For TopNByAttribution, use:

{
  filterType = "TopNByAttribution"
  top = int
}

MonitorComputeConfigurationBase objects

Set the computeType property to specify the type of object.

For ServerlessSpark, use:

{
  computeIdentity = {
    computeIdentityType = "string"
    // For remaining properties, see MonitorComputeIdentityBase objects
  }
  computeType = "ServerlessSpark"
  instanceType = "string"
  runtimeVersion = "string"
}

Property values

AllFeatures

Name	Description	Value
filterType	[Required] Specifies the feature filter to leverage when selecting features to calculate metrics over.	'AllFeatures' (required)

AllNodes

Name	Description	Value
nodesValueType	[Required] Type of the Nodes value	'All' (required)

AmlToken

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'AMLToken' (required)

AmlTokenComputeIdentity

Name	Description	Value
computeIdentityType	[Required] Specifies the type of identity to use within the monitoring jobs.	'AmlToken' (required)

AutoForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	'Auto' (required)

AutoMLJob

Name	Description	Value
environmentId	The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.	string
environmentVariables	Environment variables included in the job.	AutoMLJobEnvironmentVariables
jobType	[Required] Specifies the type of job.	'AutoML' (required)
outputs	Mapping of output data bindings used in the job.	AutoMLJobOutputs
queueSettings	Queue settings for the job	QueueSettings
resources	Compute Resource configuration for the job.	JobResourceConfiguration
taskDetails	[Required] This represents scenario which can be one of Tables/NLP/Image	AutoMLVertical (required)

AutoMLJobEnvironmentVariables

Name	Description	Value

AutoMLJobOutputs

Name	Description	Value

AutoMLVertical

Name	Description	Value
logVerbosity	Log verbosity for the job.	'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning'
targetColumnName	Target column name: This is prediction values column. Also known as label column name in context of classification tasks.	string
taskType	Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer.	'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required)
trainingData	[Required] Training data input.	MLTableJobInput (required)

AutoNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	'Auto' (required)

AutoSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	'Auto' (required)

AutoTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	'Auto' (required)

AutoTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	'Auto' (required)

AzureDevOpsWebhook

Name	Description	Value
webhookType	[Required] Specifies the type of service to send a callback	'AzureDevOps' (required)

AzureOpenAiFineTuning

Name	Description	Value
hyperParameters	HyperParameters for fine tuning Azure Open AI model.	AzureOpenAiHyperParameters
modelProvider	[Required] Enum to determine the type of fine tuning.	'AzureOpenAI' (required)

AzureOpenAiHyperParameters

Name	Description	Value
batchSize	Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.	int
learningRateMultiplier	Scaling factor for the learning rate. A smaller learning rate may be useful to avoid over fitting.	int
nEpochs	The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.	int

BanditPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'Bandit' (required)
slackAmount	Absolute distance allowed from the best performing run.	int
slackFactor	Ratio of the allowed distance from the best performing run.	int

BayesianSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Bayesian' (required)

CategoricalDataDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Categorical' (required)
metric	[Required] The categorical data drift metric to calculate.	'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required)

CategoricalDataQualityMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Categorical' (required)
metric	[Required] The categorical data quality metric to calculate.	'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required)

CategoricalPredictionDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Categorical' (required)
metric	[Required] The categorical prediction drift metric to calculate.	'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required)

Classification

Name	Description	Value
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
positiveLabel	Positive label for binary metrics calculation.	string
primaryMetric	Primary metric for the task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
taskType	[Required] Task type for AutoMLJob.	'Classification' (required)
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ClassificationTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

ClassificationTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for classification task.	String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier'
blockedTrainingAlgorithms	Blocked models for classification task.	String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings

ColumnTransformer

Name	Description	Value
fields	Fields to apply transformer logic on.	string[]
parameters	Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.	any

CommandJob

Name	Description	Value
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
environmentVariables	Environment variables included in the job.	CommandJobEnvironmentVariables
inputs	Mapping of input data bindings used in the job.	CommandJobInputs
jobType	[Required] Specifies the type of job.	'Command' (required)
limits	Command Job limit.	CommandJobLimits
outputs	Mapping of output data bindings used in the job.	CommandJobOutputs
queueSettings	Queue settings for the job	QueueSettings
resources	Compute Resource configuration for the job.	JobResourceConfiguration

CommandJobEnvironmentVariables

Name	Description	Value

CommandJobInputs

Name	Description	Value

CommandJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	'Command' 'Sweep' (required)
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string

CommandJobOutputs

Name	Description	Value

CreateMonitorAction

Name	Description	Value
actionType	[Required] Specifies the action type of the schedule	'CreateMonitor' (required)
monitorDefinition	[Required] Defines the monitor.	MonitorDefinition (required)

CronTrigger

Name	Description	Value
expression	[Required] Specifies cron expression of schedule. The expression should follow NCronTab format.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
triggerType	[Required]	'Cron' (required)

CustomForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	'Custom' (required)
value	[Required] Forecast horizon value.	int (required)

CustomMetricThreshold

Name	Description	Value
metric	[Required] The user-defined metric to calculate.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

CustomModelFineTuning

Name	Description	Value
hyperParameters	HyperParameters for fine tuning custom model.	CustomModelFineTuningHyperParameters
modelProvider	[Required] Enum to determine the type of fine tuning.	'Custom' (required)

CustomModelFineTuningHyperParameters

Name	Description	Value

CustomModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'custom_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

CustomModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'custom_model' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

CustomMonitoringSignal

Name	Description	Value
componentId	[Required] Reference to the component asset used to calculate the custom metrics.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
inputAssets	Monitoring assets to take as input. Key is the component input port name, value is the data asset.	CustomMonitoringSignalInputAssets
inputs	Extra component parameters to take as input. Key is the component literal input port name, value is the parameter value.	CustomMonitoringSignalInputs
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	CustomMetricThreshold[] (required)
signalType	[Required] Specifies the type of signal to monitor.	'Custom' (required)

CustomMonitoringSignalInputAssets

Name	Description	Value

CustomMonitoringSignalInputs

Name	Description	Value

CustomNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	'Custom' (required)
value	[Required] N-Cross validations value.	int (required)

CustomSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	'Custom' (required)
value	[Required] Seasonality value.	int (required)

CustomTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	'Custom' (required)
values	[Required] Set target lags values.	int[] (required)

CustomTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	'Custom' (required)
value	[Required] TargetRollingWindowSize value.	int (required)

DataDriftMetricThresholdBase

Name	Description	Value
dataType	Set to 'Categorical' for type CategoricalDataDriftMetricThreshold. Set to 'Numerical' for type NumericalDataDriftMetricThreshold.	'Categorical' 'Numerical' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

DataDriftMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	DataDriftMonitoringSignalFeatureDataTypeOverride
featureImportanceSettings	The settings for computing feature importance.	FeatureImportanceSettings
features	The feature filter which identifies which feature to calculate drift over.	MonitoringFeatureFilterBase
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	DataDriftMetricThresholdBase[] (required)
productionData	[Required] The data which drift will be calculated for.	MonitoringInputDataBase (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'DataDrift' (required)

DataDriftMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

DataQualityMetricThresholdBase

Name	Description	Value
dataType	Set to 'Categorical' for type CategoricalDataQualityMetricThreshold. Set to 'Numerical' for type NumericalDataQualityMetricThreshold.	'Categorical' 'Numerical' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

DataQualityMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	DataQualityMonitoringSignalFeatureDataTypeOverride
featureImportanceSettings	The settings for computing feature importance.	FeatureImportanceSettings
features	The features to calculate drift over.	MonitoringFeatureFilterBase
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	DataQualityMetricThresholdBase[] (required)
productionData	[Required] The data produced by the production service which drift will be calculated for.	MonitoringInputDataBase (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'DataQuality' (required)

DataQualityMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

DistributionConfiguration

Name	Description	Value
distributionType	Set to 'Mpi' for type Mpi. Set to 'PyTorch' for type PyTorch. Set to 'TensorFlow' for type TensorFlow.	'Mpi' 'PyTorch' 'TensorFlow' (required)

EarlyTerminationPolicy

Name	Description	Value
delayEvaluation	Number of intervals by which to delay the first evaluation.	int
evaluationInterval	Interval (number of runs) between policy evaluations.	int
policyType	Set to 'Bandit' for type BanditPolicy. Set to 'MedianStopping' for type MedianStoppingPolicy. Set to 'TruncationSelection' for type TruncationSelectionPolicy.	'Bandit' 'MedianStopping' 'TruncationSelection' (required)

EndpointScheduleAction

Name	Description	Value
actionType	[Required] Specifies the action type of the schedule	'InvokeBatchEndpoint' (required)
endpointInvocationDefinition	[Required] Defines Schedule action definition details. <see href="TBD" />	any (required)

FeatureAttributionDriftMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride
featureImportanceSettings	[Required] The settings for computing feature importance.	FeatureImportanceSettings (required)
metricThreshold	[Required] A list of metrics to calculate and their associated thresholds.	FeatureAttributionMetricThreshold (required)
productionData	[Required] The data which drift will be calculated for.	MonitoringInputDataBase[] (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'FeatureAttributionDrift' (required)

FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

FeatureAttributionMetricThreshold

Name	Description	Value
metric	[Required] The feature attribution metric to calculate.	'NormalizedDiscountedCumulativeGain' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

FeatureImportanceSettings

Name	Description	Value
mode	The mode of operation for computing feature importance.	'Disabled' 'Enabled'
targetColumn	The name of the target column within the input data asset.	string

FeatureSubset

Name	Description	Value
features	[Required] The list of features to include.	string[] (required)
filterType	[Required] Specifies the feature filter to leverage when selecting features to calculate metrics over.	'FeatureSubset' (required)

FineTuningJob

Name	Description	Value
fineTuningDetails	[Required]	FineTuningVertical (required)
jobType	[Required] Specifies the type of job.	'FineTuning' (required)
outputs	[Required]	FineTuningJobOutputs (required)
queueSettings	Queue settings for the job	QueueSettings
resources	Instance types and other resources for the job	JobResources

FineTuningJobOutputs

Name	Description	Value

FineTuningVertical

Name	Description	Value
model	[Required] Input model for fine tuning.	MLFlowModelJobInput (required)
modelProvider	Set to 'AzureOpenAI' for type AzureOpenAiFineTuning. Set to 'Custom' for type CustomModelFineTuning.	'AzureOpenAI' 'Custom' (required)
taskType	[Required] Fine tuning task type.	'ChatCompletion' 'ImageClassification' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'QuestionAnswering' 'TextClassification' 'TextCompletion' 'TextSummarization' 'TextTranslation' 'TokenClassification' 'VideoMultiObjectTracking' (required)
trainingData	[Required] Training data for fine tuning.	JobInput (required)
validationData	Validation data for fine tuning.	JobInput

FixedInputData

Name	Description	Value
inputDataType	[Required] Specifies the type of signal to monitor.	'Fixed' (required)

ForecastHorizon

Name	Description	Value
mode	Set to 'Auto' for type AutoForecastHorizon. Set to 'Custom' for type CustomForecastHorizon.	'Auto' 'Custom' (required)

Forecasting

Name	Description	Value
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
forecastingSettings	Forecasting task specific inputs.	ForecastingSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for forecasting task.	'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation'
taskType	[Required] Task type for AutoMLJob.	'Forecasting' (required)
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ForecastingTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

ForecastingSettings

Name	Description	Value
countryOrRegionForHolidays	Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.	string
cvStepSize	Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.	int
featureLags	Flag for generating lags for the numeric features with 'auto' or null.	'Auto' 'None'
forecastHorizon	The desired maximum forecast horizon in units of time-series frequency.	ForecastHorizon
frequency	When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.	string
seasonality	Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.	Seasonality
shortSeriesHandlingConfig	The parameter defining how if AutoML should handle short time series.	'Auto' 'Drop' 'None' 'Pad'
targetAggregateFunction	The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".	'Max' 'Mean' 'Min' 'None' 'Sum'
targetLags	The number of past periods to lag from the target column.	TargetLags
targetRollingWindowSize	The number of past periods used to create a rolling window average of the target column.	TargetRollingWindowSize
timeColumnName	The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.	string
timeSeriesIdColumnNames	The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.	string[]
useStl	Configure STL Decomposition of the time-series target column.	'None' 'Season' 'SeasonTrend'

ForecastingTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for forecasting task.	String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor'
blockedTrainingAlgorithms	Blocked models for forecasting task.	String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings

GridSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Grid' (required)

IdentityConfiguration

Name	Description	Value
identityType	Set to 'AMLToken' for type AmlToken. Set to 'Managed' for type ManagedIdentity. Set to 'UserIdentity' for type UserIdentity.	'AMLToken' 'Managed' 'UserIdentity' (required)

ImageClassification

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageClassification' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageClassificationMultilabel

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'IOU' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageClassificationMultilabel' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageInstanceSegmentation

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	'MeanAveragePrecision'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageInstanceSegmentation' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum number of concurrent AutoML iterations.	int
maxTrials	Maximum number of AutoML iterations.	int
timeout	AutoML job timeout.	string

ImageModelDistributionSettingsClassification

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	string
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	string

ImageModelDistributionSettingsObjectDetection

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	string
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	string
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	string
validationMetricType	Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string

ImageModelSettingsClassification

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	'None' 'Step' 'WarmupCosine'
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	'Adam' 'Adamw' 'None' 'Sgd'
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	int
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	int
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	int

ImageModelSettingsObjectDetection

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	int
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	'None' 'Step' 'WarmupCosine'
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	'ExtraLarge' 'Large' 'Medium' 'None' 'Small'
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	bool
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].	int
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	'Adam' 'Adamw' 'None' 'Sgd'
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	int
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	int
validationMetricType	Metric computation method to use for validation metrics.	'Coco' 'CocoVoc' 'None' 'Voc'
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int

ImageObjectDetection

Name	Description	Value
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	'MeanAveragePrecision'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
taskType	[Required] Task type for AutoMLJob.	'ImageObjectDetection' (required)
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of the hyperparameter sampling algorithms.	'Bayesian' 'Grid' 'Random' (required)

JobBaseProperties

Name	Description	Value
componentId	ARM resource ID of the component resource.	string
computeId	ARM resource ID of the compute resource.	string
description	The asset description text.	string
displayName	Display name of job.	string
experimentName	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.	string
identity	Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.	IdentityConfiguration
isArchived	Is the asset archived?	bool
jobType	Set to 'AutoML' for type AutoMLJob. Set to 'Command' for type CommandJob. Set to 'FineTuning' for type FineTuningJob. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob.	'AutoML' 'Command' 'FineTuning' 'Pipeline' 'Spark' 'Sweep' (required)
notificationSetting	Notification setting for the job	NotificationSetting
properties	The asset property dictionary.	ResourceBaseProperties
services	List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.	JobBaseServices
tags	Tag dictionary. Tags can be added, removed, and updated.	ResourceBaseTags

JobBaseServices

Name	Description	Value

JobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	Set to 'custom_model' for type CustomModelJobInput. Set to 'literal' for type LiteralJobInput. Set to 'mlflow_model' for type MLFlowModelJobInput. Set to 'mltable' for type MLTableJobInput. Set to 'triton_model' for type TritonModelJobInput. Set to 'uri_file' for type UriFileJobInput. Set to 'uri_folder' for type UriFolderJobInput.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)

JobOutput

Name	Description	Value
description	Description for the output.	string
jobOutputType	Set to 'custom_model' for type CustomModelJobOutput. Set to 'mlflow_model' for type MLFlowModelJobOutput. Set to 'mltable' for type MLTableJobOutput. Set to 'triton_model' for type TritonModelJobOutput. Set to 'uri_file' for type UriFileJobOutput. Set to 'uri_folder' for type UriFolderJobOutput.	'custom_model' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)

JobResourceConfiguration

Name	Description	Value
dockerArgs	Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.	string
instanceCount	Optional number of instances or nodes used by the compute target.	int
instanceType	Optional type of VM used as supported by the compute target.	string
properties	Additional properties bag.	ResourceConfigurationProperties
shmSize	Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).	string Constraints: Pattern = \d+[bBkKmMgG]

JobResources

Name	Description	Value
instanceTypes	List of instance types to choose from.	string[]

JobScheduleAction

Name	Description	Value
actionType	[Required] Specifies the action type of the schedule	'CreateJob' (required)
jobDefinition	[Required] Defines Schedule action definition details.	JobBaseProperties (required)

JobService

Name	Description	Value
endpoint	Url for endpoint.	string
jobServiceType	Endpoint type.	string
nodes	Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.	Nodes
port	Port for endpoint.	int
properties	Additional properties to set on the endpoint.	JobServiceProperties

JobServiceProperties

Name	Description	Value

LiteralJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'literal' (required)
value	[Required] Literal value for the input.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

ManagedComputeIdentity

Name	Description	Value
computeIdentityType	[Required] Specifies the type of identity to use within the monitoring jobs.	'ManagedIdentity' (required)
identity	The identity which will be leveraged by the monitoring jobs.	ManagedServiceIdentity

ManagedIdentity

Name	Description	Value
clientId	Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
identityType	[Required] Specifies the type of identity framework.	'Managed' (required)
objectId	Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
resourceId	Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.	string

ManagedServiceIdentity

Name	Description	Value
type	Type of managed service identity (where both SystemAssigned and UserAssigned types are allowed).	'None' 'SystemAssigned' 'SystemAssigned,UserAssigned' 'UserAssigned' (required)
userAssignedIdentities	The set of user assigned identities associated with the resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form: '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}. The dictionary values can be empty objects ({}) in requests.	UserAssignedIdentities

MedianStoppingPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'MedianStopping' (required)

Microsoft.MachineLearningServices/workspaces/schedules

Name	Description	Value
name	The resource name	string Constraints: Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (required)
parent_id	The ID of the resource that is the parent for this resource.	ID for resource of type: workspaces
properties	[Required] Additional attributes of the entity.	ScheduleProperties (required)
type	The resource type	"Microsoft.MachineLearningServices/workspaces/schedules@2024-07-01-preview"

MLFlowModelJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLFlowModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'mlflow_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLFlowModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'mlflow_model' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

MLTableJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLTableJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'mltable' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MLTableJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'mltable' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

MonitorComputeConfigurationBase

Name	Description	Value
computeType	Set to 'ServerlessSpark' for type MonitorServerlessSparkCompute.	'ServerlessSpark' (required)

MonitorComputeIdentityBase

Name	Description	Value
computeIdentityType	Set to 'AmlToken' for type AmlTokenComputeIdentity. Set to 'ManagedIdentity' for type ManagedComputeIdentity.	'AmlToken' 'ManagedIdentity' (required)

MonitorDefinition

Name	Description	Value
alertNotificationSettings	The monitor's notification settings.	MonitorNotificationSettings
computeConfiguration	[Required] The ARM resource ID of the compute resource to run the monitoring job on.	MonitorComputeConfigurationBase (required)
monitoringTarget	The entities targeted by the monitor.	MonitoringTarget
signals	[Required] The signals to monitor.	MonitorDefinitionSignals (required)

MonitorDefinitionSignals

Name	Description	Value

MonitorEmailNotificationSettings

Name	Description	Value
emails	The email recipient list which has a limitation of 499 characters in total.	string[]

MonitoringFeatureFilterBase

Name	Description	Value
filterType	Set to 'AllFeatures' for type AllFeatures. Set to 'FeatureSubset' for type FeatureSubset. Set to 'TopNByAttribution' for type TopNFeaturesByAttribution.	'AllFeatures' 'FeatureSubset' 'TopNByAttribution' (required)

MonitoringInputDataBase

Name	Description	Value
columns	Mapping of column names to special uses.	MonitoringInputDataBaseColumns
dataContext	The context metadata of the data source.	string
inputDataType	Set to 'Fixed' for type FixedInputData. Set to 'Rolling' for type RollingInputData. Set to 'Static' for type StaticInputData.	'Fixed' 'Rolling' 'Static' (required)
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

MonitoringInputDataBaseColumns

Name	Description	Value

MonitoringSignalBase

Name	Description	Value
notificationTypes	The current notification mode for this signal.	String array containing any of: 'AmlNotification'
properties	Property dictionary. Properties can be added, but not removed or altered.	MonitoringSignalBaseProperties
signalType	Set to 'Custom' for type CustomMonitoringSignal. Set to 'DataDrift' for type DataDriftMonitoringSignal. Set to 'DataQuality' for type DataQualityMonitoringSignal. Set to 'FeatureAttributionDrift' for type FeatureAttributionDriftMonitoringSignal. Set to 'PredictionDrift' for type PredictionDriftMonitoringSignal.	'Custom' 'DataDrift' 'DataQuality' 'FeatureAttributionDrift' 'PredictionDrift' (required)

MonitoringSignalBaseProperties

Name	Description	Value

MonitoringTarget

Name	Description	Value
deploymentId	Reference to the deployment asset targeted by this monitor.	string
modelId	Reference to the model asset targeted by this monitor.	string
taskType	[Required] The machine learning task type of the monitored model.	'Classification' 'Regression' (required)

MonitoringThreshold

Name	Description	Value
value	The threshold value. If null, the set default is dependent on the metric type.	int

MonitorNotificationSettings

Name	Description	Value
emailNotificationSettings	The AML notification email settings.	MonitorEmailNotificationSettings

MonitorServerlessSparkCompute

Name	Description	Value
computeIdentity	[Required] The identity scheme leveraged to by the spark jobs running on serverless Spark.	MonitorComputeIdentityBase (required)
computeType	[Required] Specifies the type of signal to monitor.	'ServerlessSpark' (required)
instanceType	[Required] The instance type running the Spark job.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
runtimeVersion	[Required] The Spark runtime version.	string Constraints: Min length = 1 Pattern = ^[0-9]+\.[0-9]+$ (required)

Mpi

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'Mpi' (required)
processCountPerInstance	Number of processes per MPI node.	int

NCrossValidations

Name	Description	Value
mode	Set to 'Auto' for type AutoNCrossValidations. Set to 'Custom' for type CustomNCrossValidations.	'Auto' 'Custom' (required)

NlpVerticalFeaturizationSettings

Name	Description	Value
datasetLanguage	Dataset language, useful for the text data.	string

NlpVerticalLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum Concurrent AutoML iterations.	int
maxTrials	Number of AutoML iterations.	int
timeout	AutoML job timeout.	string

Nodes

Name	Description	Value
nodesValueType	Set to 'All' for type AllNodes.	'All' (required)

NotificationSetting

Name	Description	Value
emailOn	Send email notification to user on specified notification type	String array containing any of: 'JobCancelled' 'JobCompleted' 'JobFailed'
emails	This is the email recipient list which has a limitation of 499 characters in total concat with comma separator	string[]
webhooks	Send webhook callback to a service. Key is a user-provided name for the webhook.	NotificationSettingWebhooks

NotificationSettingWebhooks

Name	Description	Value

NumericalDataDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Numerical' (required)
metric	[Required] The numerical data drift metric to calculate.	'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required)

NumericalDataQualityMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Numerical' (required)
metric	[Required] The numerical data quality metric to calculate.	'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required)

NumericalPredictionDriftMetricThreshold

Name	Description	Value
dataType	[Required] Specifies the data type of the metric threshold.	'Numerical' (required)
metric	[Required] The numerical prediction drift metric to calculate.	'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required)

Objective

Name	Description	Value
goal	[Required] Defines supported metric goals for hyperparameter tuning	'Maximize' 'Minimize' (required)
primaryMetric	[Required] Name of the metric to optimize.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

PipelineJob

Name	Description	Value
inputs	Inputs for the pipeline job.	PipelineJobInputs
jobs	Jobs construct the Pipeline Job.	PipelineJobJobs
jobType	[Required] Specifies the type of job.	'Pipeline' (required)
outputs	Outputs for the pipeline job	PipelineJobOutputs
settings	Pipeline settings, for things like ContinueRunOnStepFailure etc.	any
sourceJobId	ARM resource ID of source job.	string

PipelineJobInputs

Name	Description	Value

PipelineJobJobs

Name	Description	Value

PipelineJobOutputs

Name	Description	Value

PredictionDriftMetricThresholdBase

Name	Description	Value
dataType	Set to 'Categorical' for type CategoricalPredictionDriftMetricThreshold. Set to 'Numerical' for type NumericalPredictionDriftMetricThreshold.	'Categorical' 'Numerical' (required)
threshold	The threshold value. If null, a default value will be set depending on the selected metric.	MonitoringThreshold

PredictionDriftMonitoringSignal

Name	Description	Value
featureDataTypeOverride	A dictionary that maps feature names to their respective data types.	PredictionDriftMonitoringSignalFeatureDataTypeOverride
metricThresholds	[Required] A list of metrics to calculate and their associated thresholds.	PredictionDriftMetricThresholdBase[] (required)
productionData	[Required] The data which drift will be calculated for.	MonitoringInputDataBase (required)
referenceData	[Required] The data to calculate drift against.	MonitoringInputDataBase (required)
signalType	[Required] Specifies the type of signal to monitor.	'PredictionDrift' (required)

PredictionDriftMonitoringSignalFeatureDataTypeOverride

Name	Description	Value

PyTorch

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'PyTorch' (required)
processCountPerInstance	Number of processes per node.	int

QueueSettings

Name	Description	Value
jobTier	Controls the compute job tier	'Basic' 'Null' 'Premium' 'Spot' 'Standard'

RandomSamplingAlgorithm

Name	Description	Value
rule	The specific type of random algorithm	'Random' 'Sobol'
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Random' (required)
seed	An optional integer to use as the seed for random number generation	int

RecurrenceSchedule

Name	Description	Value
hours	[Required] List of hours for the schedule.	int[] (required)
minutes	[Required] List of minutes for the schedule.	int[] (required)
monthDays	List of month days for the schedule	int[]
weekDays	List of days for the schedule.	String array containing any of: 'Friday' 'Monday' 'Saturday' 'Sunday' 'Thursday' 'Tuesday' 'Wednesday'

RecurrenceTrigger

Name	Description	Value
frequency	[Required] The frequency to trigger schedule.	'Day' 'Hour' 'Minute' 'Month' 'Week' (required)
interval	[Required] Specifies schedule interval in conjunction with frequency	int (required)
schedule	The recurrence schedule.	RecurrenceSchedule
triggerType	[Required]	'Recurrence' (required)

Regression

Name	Description	Value
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for regression task.	'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation'
taskType	[Required] Task type for AutoMLJob.	'Regression' (required)
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	RegressionTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

RegressionTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for regression task.	String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor'
blockedTrainingAlgorithms	Blocked models for regression task.	String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings

ResourceBaseProperties

Name	Description	Value

ResourceBaseProperties

Name	Description	Value

ResourceBaseTags

Name	Description	Value

ResourceBaseTags

Name	Description	Value

ResourceConfigurationProperties

Name	Description	Value

RollingInputData

Name	Description	Value
inputDataType	[Required] Specifies the type of signal to monitor.	'Rolling' (required)
preprocessingComponentId	Reference to the component asset used to preprocess the data.	string
windowOffset	[Required] The time offset between the end of the data window and the monitor's current run time.	string (required)
windowSize	[Required] The size of the rolling data window.	string (required)

SamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	Set to 'Bayesian' for type BayesianSamplingAlgorithm. Set to 'Grid' for type GridSamplingAlgorithm. Set to 'Random' for type RandomSamplingAlgorithm.	'Bayesian' 'Grid' 'Random' (required)

ScheduleActionBase

Name	Description	Value
actionType	Set to 'CreateJob' for type JobScheduleAction. Set to 'CreateMonitor' for type CreateMonitorAction. Set to 'InvokeBatchEndpoint' for type EndpointScheduleAction.	'CreateJob' 'CreateMonitor' 'InvokeBatchEndpoint' (required)

ScheduleProperties

Name	Description	Value
action	[Required] Specifies the action of the schedule	ScheduleActionBase (required)
description	The asset description text.	string
displayName	Display name of schedule.	string
isEnabled	Is the schedule enabled?	bool
properties	The asset property dictionary.	ResourceBaseProperties
tags	Tag dictionary. Tags can be added, removed, and updated.	ResourceBaseTags
trigger	[Required] Specifies the trigger details	TriggerBase (required)

Seasonality

Name	Description	Value
mode	Set to 'Auto' for type AutoSeasonality. Set to 'Custom' for type CustomSeasonality.	'Auto' 'Custom' (required)

SparkJob

Name	Description	Value
archives	Archive files used in the job.	string[]
args	Arguments for the job.	string
codeId	[Required] arm-id of the code asset.	string (required)
conf	Spark configured properties.	SparkJobConf
entry	[Required] The entry to execute on startup of the job.	SparkJobEntry (required)
environmentId	The ARM resource ID of the Environment specification for the job.	string
environmentVariables	Environment variables included in the job.	SparkJobEnvironmentVariables
files	Files used in the job.	string[]
inputs	Mapping of input data bindings used in the job.	SparkJobInputs
jars	Jar files used in the job.	string[]
jobType	[Required] Specifies the type of job.	'Spark' (required)
outputs	Mapping of output data bindings used in the job.	SparkJobOutputs
pyFiles	Python files used in the job.	string[]
queueSettings	Queue settings for the job	QueueSettings
resources	Compute Resource configuration for the job.	SparkResourceConfiguration

SparkJobConf

Name	Description	Value

SparkJobEntry

Name	Description	Value
sparkJobEntryType	Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry.	'SparkJobPythonEntry' 'SparkJobScalaEntry' (required)

SparkJobEnvironmentVariables

Name	Description	Value

SparkJobInputs

Name	Description	Value

SparkJobOutputs

Name	Description	Value

SparkJobPythonEntry

Name	Description	Value
file	[Required] Relative python file path for job entry point.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
sparkJobEntryType	[Required] Type of the job's entry point.	'SparkJobPythonEntry' (required)

SparkJobScalaEntry

Name	Description	Value
className	[Required] Scala class name used as entry point.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
sparkJobEntryType	[Required] Type of the job's entry point.	'SparkJobScalaEntry' (required)

SparkResourceConfiguration

Name	Description	Value
instanceType	Optional type of VM used as supported by the compute target.	string
runtimeVersion	Version of spark runtime used for the job.	string

StackEnsembleSettings

Name	Description	Value
stackMetaLearnerKWargs	Optional parameters to pass to the initializer of the meta-learner.	any
stackMetaLearnerTrainPercentage	Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.	int
stackMetaLearnerType	The meta-learner is a model trained on the output of the individual heterogeneous models.	'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None'

StaticInputData

Name	Description	Value
inputDataType	[Required] Specifies the type of signal to monitor.	'Static' (required)
preprocessingComponentId	Reference to the component asset used to preprocess the data.	string
windowEnd	[Required] The end date of the data window.	string (required)
windowStart	[Required] The start date of the data window.	string (required)

SweepJob

Name	Description	Value
earlyTermination	Early termination policies enable canceling poor-performing runs before they complete	EarlyTerminationPolicy
inputs	Mapping of input data bindings used in the job.	SweepJobInputs
jobType	[Required] Specifies the type of job.	'Sweep' (required)
limits	Sweep Job limit.	SweepJobLimits
objective	[Required] Optimization objective.	Objective (required)
outputs	Mapping of output data bindings used in the job.	SweepJobOutputs
queueSettings	Queue settings for the job	QueueSettings
samplingAlgorithm	[Required] The hyperparameter sampling algorithm	SamplingAlgorithm (required)
searchSpace	[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter	any (required)
trial	[Required] Trial component definition.	TrialComponent (required)

SweepJobInputs

Name	Description	Value

SweepJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	'Command' 'Sweep' (required)
maxConcurrentTrials	Sweep Job max concurrent trials.	int
maxTotalTrials	Sweep Job max total trials.	int
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string
trialTimeout	Sweep Job Trial timeout value.	string

SweepJobOutputs

Name	Description	Value

TableVerticalFeaturizationSettings

Name	Description	Value
blockedTransformers	These transformers shall not be used in featurization.	String array containing any of: 'CatTargetEncoder' 'CountVectorizer' 'HashOneHotEncoder' 'LabelEncoder' 'NaiveBayes' 'OneHotEncoder' 'TextTargetEncoder' 'TfIdf' 'WoETargetEncoder' 'WordEmbedding'
columnNameAndTypes	Dictionary of column name and its type (int, float, string, datetime etc).	TableVerticalFeaturizationSettingsColumnNameAndTypes
datasetLanguage	Dataset language, useful for the text data.	string
enableDnnFeaturization	Determines whether to use Dnn based featurizers for data featurization.	bool
mode	Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.	'Auto' 'Custom' 'Off'
transformerParams	User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.	TableVerticalFeaturizationSettingsTransformerParams

TableVerticalFeaturizationSettingsColumnNameAndTypes

Name	Description	Value

TableVerticalFeaturizationSettingsTransformerParams

Name	Description	Value

TableVerticalLimitSettings

Name	Description	Value
enableEarlyTermination	Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.	bool
exitScore	Exit score for the AutoML job.	int
maxConcurrentTrials	Maximum Concurrent iterations.	int
maxCoresPerTrial	Max cores per iteration.	int
maxTrials	Number of iterations.	int
timeout	AutoML job timeout.	string
trialTimeout	Iteration timeout.	string

TargetLags

Name	Description	Value
mode	Set to 'Auto' for type AutoTargetLags. Set to 'Custom' for type CustomTargetLags.	'Auto' 'Custom' (required)

TargetRollingWindowSize

Name	Description	Value
mode	Set to 'Auto' for type AutoTargetRollingWindowSize. Set to 'Custom' for type CustomTargetRollingWindowSize.	'Auto' 'Custom' (required)

TensorFlow

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'TensorFlow' (required)
parameterServerCount	Number of parameter server tasks.	int
workerCount	Number of workers. If not specified, will default to the instance count.	int

TextClassification

Name	Description	Value
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
primaryMetric	Primary metric for Text-Classification task.	'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
taskType	[Required] Task type for AutoMLJob.	'TextClassification' (required)
validationData	Validation data inputs.	MLTableJobInput

TextClassificationMultilabel

Name	Description	Value
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
taskType	[Required] Task type for AutoMLJob.	'TextClassificationMultilabel' (required)
validationData	Validation data inputs.	MLTableJobInput

TextNer

Name	Description	Value
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
taskType	[Required] Task type for AutoMLJob.	'TextNER' (required)
validationData	Validation data inputs.	MLTableJobInput

TopNFeaturesByAttribution

Name	Description	Value
filterType	[Required] Specifies the feature filter to leverage when selecting features to calculate metrics over.	'TopNByAttribution' (required)
top	The number of top features to include.	int

TrialComponent

Name	Description	Value
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)
environmentVariables	Environment variables included in the job.	TrialComponentEnvironmentVariables
resources	Compute Resource configuration for the job.	JobResourceConfiguration

TrialComponentEnvironmentVariables

Name	Description	Value

TriggerBase

Name	Description	Value
endTime	Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely	string
startTime	Specifies start time of schedule in ISO 8601 format, but without a UTC offset.	string
timeZone	Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones?view=windows-11	string
triggerType	Set to 'Cron' for type CronTrigger. Set to 'Recurrence' for type RecurrenceTrigger.	'Cron' 'Recurrence' (required)

TritonModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'triton_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

TritonModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'triton_model' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

TruncationSelectionPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'TruncationSelection' (required)
truncationPercentage	The percentage of runs to cancel at each evaluation interval.	int

UriFileJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'uri_file' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

UriFileJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'uri_file' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

UriFolderJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required)

UriFolderJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'uri_folder' (required)
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

UserAssignedIdentities

Name	Description	Value

UserAssignedIdentity

Name	Description	Value

UserIdentity

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'UserIdentity' (required)

Webhook

Name	Description	Value
eventType	Send callback on a specified notification event	string
webhookType	Set to 'AzureDevOps' for type AzureDevOpsWebhook.	'AzureDevOps' (required)