MonitorDefinition Class

Monitor definition

Constructor

MonitorDefinition(*, compute: ServerlessSparkCompute, monitoring_target: MonitoringTarget | None = None, monitoring_signals: Dict[str, DataDriftSignal | DataQualitySignal | PredictionDriftSignal | FeatureAttributionDriftSignal | CustomMonitoringSignal | GenerationSafetyQualitySignal | GenerationTokenStatisticsSignal] = None, alert_notification: Literal['azmonitoring'] | AlertNotification | None = None)

Keyword-Only Parameters

Name	Description
compute	SparkResourceConfiguration The Spark resource configuration to be associated with the monitor
monitoring_target	Optional[MonitoringTarget] The ARM ID object associated with the model or deployment that is being monitored.
monitoring_signals	Optional[Dict[str, Union[DataDriftSignal , DataQualitySignal, PredictionDriftSignal , FeatureAttributionDriftSignal , CustomMonitoringSignal , GenerationSafetyQualitySignal , GenerationTokenStatisticsSignal , ModelPerformanceSignal]]] The dictionary of signals to monitor. The key is the name of the signal and the value is the DataSignal object. Accepted values for the DataSignal objects are DataDriftSignal, DataQualitySignal, PredictionDriftSignal, FeatureAttributionDriftSignal, and CustomMonitoringSignal.
alert_notification	Optional[Union[Literal['azmonitoring'], azure.ai.ml.entities.AlertNotification]] The alert configuration for the monitor.

Examples

Creating Monitor definition.

   from azure.ai.ml.entities import (
       AlertNotification,
       MonitorDefinition,
       MonitoringTarget,
       SparkResourceConfiguration,
   )

   monitor_definition = MonitorDefinition(
       compute=SparkResourceConfiguration(instance_type="standard_e4s_v3", runtime_version="3.4"),
       monitoring_target=MonitoringTarget(
           ml_task="Classification",
           endpoint_deployment_id="azureml:fraud_detection_endpoint:fraud_detection_deployment",
       ),
       alert_notification=AlertNotification(emails=["abc@example.com", "def@example.com"]),
   )