Microsoft.MachineLearningServices workspaces 2019-06-01
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Bicep resource definition
The workspaces 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 resource, add the following Bicep to your template.
resource symbolicname 'Microsoft.MachineLearningServices/workspaces@2019-06-01' = {
identity: {
type: 'SystemAssigned'
}
location: 'string'
name: 'string'
properties: {
applicationInsights: 'string'
containerRegistry: 'string'
description: 'string'
discoveryUrl: 'string'
friendlyName: 'string'
keyVault: 'string'
storageAccount: 'string'
}
tags: {
{customized property}: 'string'
}
}
Property values
Identity
| Name | Description | Value |
|---|---|---|
| type | The identity type. | 'SystemAssigned' |
Microsoft.MachineLearningServices/workspaces
| Name | Description | Value |
|---|---|---|
| identity | The identity of the resource. | Identity |
| location | Specifies the location of the resource. | string |
| name | The resource name | string (required) |
| properties | The properties of the machine learning workspace. | WorkspaceProperties |
| tags | Resource tags | Dictionary of tag names and values. See Tags in templates |
ResourceTags
| Name | Description | Value |
|---|
WorkspaceProperties
| Name | Description | Value |
|---|---|---|
| applicationInsights | ARM id of the application insights associated with this workspace. This cannot be changed once the workspace has been created | string |
| containerRegistry | ARM id of the container registry associated with this workspace. This cannot be changed once the workspace has been created | string |
| description | The description of this workspace. | string |
| discoveryUrl | Url for the discovery service to identify regional endpoints for machine learning experimentation services | string |
| friendlyName | The friendly name for this workspace. This name in mutable | string |
| keyVault | ARM id of the key vault associated with this workspace. This cannot be changed once the workspace has been created | string |
| storageAccount | ARM id of the storage account associated with this workspace. This cannot be changed once the workspace has been created | string |
Quickstart samples
The following quickstart samples deploy this resource type.
| Bicep File | Description |
|---|---|
| Azure AI Studio basic setup | This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio basic setup | This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio basic setup | This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio Network Restricted | This set of templates demonstrates how to set up Azure AI Studio with private link and egress disabled, using Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio Network Restricted | This set of templates demonstrates how to set up Azure AI Studio with private link and egress disabled, using Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio with Microsoft Entra ID Authentication | This set of templates demonstrates how to set up Azure AI Studio with Microsoft Entra ID authentication for dependent resources, such as Azure AI Services and Azure Storage. |
| Azure Machine Learning end-to-end secure setup | This set of Bicep templates demonstrates how to set up Azure Machine Learning end-to-end in a secure set up. This reference implementation includes the Workspace, a compute cluster, compute instance and attached private AKS cluster. |
| Azure Machine Learning end-to-end secure setup (legacy) | This set of Bicep templates demonstrates how to set up Azure Machine Learning end-to-end in a secure set up. This reference implementation includes the Workspace, a compute cluster, compute instance and attached private AKS cluster. |
| Create an AKS compute target with a Private IP address | This template creates an AKS compute target in given Azure Machine Learning service workspace with a private IP address. |
| Create an Azure Machine Learning service workspace | This deployment template specifies an Azure Machine Learning workspace, and its associated resources including Azure Key Vault, Azure Storage, Azure Application Insights and Azure Container Registry. This configuration describes the minimal set of resources you require to get started with Azure Machine Learning. |
| Create an Azure Machine Learning service workspace (CMK) | This deployment template specifies how to create an Azure Machine Learning workspace with service-side encryption using your encryption keys. |
| Create an Azure Machine Learning service workspace (CMK) | This deployment template specifies an Azure Machine Learning workspace, and its associated resources including Azure Key Vault, Azure Storage, Azure Application Insights and Azure Container Registry. The example shows how to configure Azure Machine Learning for encryption with a customer-managed encryption key. |
| Create an Azure Machine Learning service workspace (legacy) | This deployment template specifies an Azure Machine Learning workspace, and its associated resources including Azure Key Vault, Azure Storage, Azure Application Insights and Azure Container Registry. This configuration describes the set of resources you require to get started with Azure Machine Learning in a network isolated set up. |
| Create an Azure Machine Learning service workspace (vnet) | This deployment template specifies an Azure Machine Learning workspace, and its associated resources including Azure Key Vault, Azure Storage, Azure Application Insights and Azure Container Registry. This configuration describes the set of resources you require to get started with Azure Machine Learning in a network isolated set up. |
| Deploy Secure Azure AI Studio with a managed virtual network | This template creates a secure Azure AI Studio environment with robust network and identity security restrictions. |
ARM template resource definition
The workspaces 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 resource, add the following JSON to your template.
{
"type": "Microsoft.MachineLearningServices/workspaces",
"apiVersion": "2019-06-01",
"name": "string",
"identity": {
"type": "SystemAssigned"
},
"location": "string",
"properties": {
"applicationInsights": "string",
"containerRegistry": "string",
"description": "string",
"discoveryUrl": "string",
"friendlyName": "string",
"keyVault": "string",
"storageAccount": "string"
},
"tags": {
"{customized property}": "string"
}
}
Property values
Identity
| Name | Description | Value |
|---|---|---|
| type | The identity type. | 'SystemAssigned' |
Microsoft.MachineLearningServices/workspaces
| Name | Description | Value |
|---|---|---|
| apiVersion | The api version | '2019-06-01' |
| identity | The identity of the resource. | Identity |
| location | Specifies the location of the resource. | string |
| name | The resource name | string (required) |
| properties | The properties of the machine learning workspace. | WorkspaceProperties |
| tags | Resource tags | Dictionary of tag names and values. See Tags in templates |
| type | The resource type | 'Microsoft.MachineLearningServices/workspaces' |
ResourceTags
| Name | Description | Value |
|---|
WorkspaceProperties
| Name | Description | Value |
|---|---|---|
| applicationInsights | ARM id of the application insights associated with this workspace. This cannot be changed once the workspace has been created | string |
| containerRegistry | ARM id of the container registry associated with this workspace. This cannot be changed once the workspace has been created | string |
| description | The description of this workspace. | string |
| discoveryUrl | Url for the discovery service to identify regional endpoints for machine learning experimentation services | string |
| friendlyName | The friendly name for this workspace. This name in mutable | string |
| keyVault | ARM id of the key vault associated with this workspace. This cannot be changed once the workspace has been created | string |
| storageAccount | ARM id of the storage account associated with this workspace. This cannot be changed once the workspace has been created | string |
Quickstart templates
The following quickstart templates deploy this resource type.
| Template | Description |
|---|---|
Azure AI Studio basic setup |
This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio basic setup |
This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio basic setup |
This set of templates demonstrates how to set up Azure AI Studio with the basic setup, meaning with public internet access enabled, Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio Network Restricted |
This set of templates demonstrates how to set up Azure AI Studio with private link and egress disabled, using Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio Network Restricted |
This set of templates demonstrates how to set up Azure AI Studio with private link and egress disabled, using Microsoft-managed keys for encryption and Microsoft-managed identity configuration for the AI resource. |
| Azure AI Studio with Microsoft Entra ID Authentication |
This set of templates demonstrates how to set up Azure AI Studio with Microsoft Entra ID authentication for dependent resources, such as Azure AI Services and Azure Storage. |
| Azure Machine Learning end-to-end secure setup |
This set of Bicep templates demonstrates how to set up Azure Machine Learning end-to-end in a secure set up. This reference implementation includes the Workspace, a compute cluster, compute instance and attached private AKS cluster. |
| Azure Machine Learning end-to-end secure setup (legacy) |
This set of Bicep templates demonstrates how to set up Azure Machine Learning end-to-end in a secure set up. This reference implementation includes the Workspace, a compute cluster, compute instance and attached private AKS cluster. |
| Azure Machine Learning Workspace |
This template creates a new Azure Machine Learning Workspace, along with an encrypted Storage Account, KeyVault and Applications Insights Logging |
| Create AML workspace with multiple Datasets & Datastores |
This template creates Azure Machine Learning workspace with multiple datasets & datastores. |
| Create an AKS compute target with a Private IP address |
This template creates an AKS compute target in given Azure Machine Learning service workspace with a private IP address. |
| Create an Azure Machine Learning service workspace |
This deployment template specifies an Azure Machine Learning workspace, and its associated resources including Azure Key Vault, Azure Storage, Azure Application Insights and Azure Container Registry. This configuration describes the minimal set of resources you require to get started with Azure Machine Learning. |
| Create an Azure Machine Learning service workspace (CMK) |
This deployment template specifies how to create an Azure Machine Learning workspace with service-side encryption using your encryption keys. |
| Create an Azure Machine Learning service workspace (CMK) |
This deployment template specifies an Azure Machine Learning workspace, and its associated resources including Azure Key Vault, Azure Storage, Azure Application Insights and Azure Container Registry. The example shows how to configure Azure Machine Learning for encryption with a customer-managed encryption key. |
| Create an Azure Machine Learning service workspace (legacy) |
This deployment template specifies an Azure Machine Learning workspace, and its associated resources including Azure Key Vault, Azure Storage, Azure Application Insights and Azure Container Registry. This configuration describes the set of resources you require to get started with Azure Machine Learning in a network isolated set up. |
| Create an Azure Machine Learning service workspace (vnet) |
This deployment template specifies an Azure Machine Learning workspace, and its associated resources including Azure Key Vault, Azure Storage, Azure Application Insights and Azure Container Registry. This configuration describes the set of resources you require to get started with Azure Machine Learning in a network isolated set up. |
| Deploy Secure Azure AI Studio with a managed virtual network |
This template creates a secure Azure AI Studio environment with robust network and identity security restrictions. |
Terraform (AzAPI provider) resource definition
The workspaces 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 resource, add the following Terraform to your template.
resource "azapi_resource" "symbolicname" {
type = "Microsoft.MachineLearningServices/workspaces@2019-06-01"
name = "string"
identity = {
type = "SystemAssigned"
}
location = "string"
tags = {
{customized property} = "string"
}
body = jsonencode({
properties = {
applicationInsights = "string"
containerRegistry = "string"
description = "string"
discoveryUrl = "string"
friendlyName = "string"
keyVault = "string"
storageAccount = "string"
}
})
}
Property values
Identity
| Name | Description | Value |
|---|---|---|
| type | The identity type. | 'SystemAssigned' |
Microsoft.MachineLearningServices/workspaces
| Name | Description | Value |
|---|---|---|
| identity | The identity of the resource. | Identity |
| location | Specifies the location of the resource. | string |
| name | The resource name | string (required) |
| properties | The properties of the machine learning workspace. | WorkspaceProperties |
| tags | Resource tags | Dictionary of tag names and values. |
| type | The resource type | "Microsoft.MachineLearningServices/workspaces@2019-06-01" |
ResourceTags
| Name | Description | Value |
|---|
WorkspaceProperties
| Name | Description | Value |
|---|---|---|
| applicationInsights | ARM id of the application insights associated with this workspace. This cannot be changed once the workspace has been created | string |
| containerRegistry | ARM id of the container registry associated with this workspace. This cannot be changed once the workspace has been created | string |
| description | The description of this workspace. | string |
| discoveryUrl | Url for the discovery service to identify regional endpoints for machine learning experimentation services | string |
| friendlyName | The friendly name for this workspace. This name in mutable | string |
| keyVault | ARM id of the key vault associated with this workspace. This cannot be changed once the workspace has been created | string |
| storageAccount | ARM id of the storage account associated with this workspace. This cannot be changed once the workspace has been created | string |