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.
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.
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.
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.
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.
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.
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.
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.
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.
This set of templates demonstrates how to set up Azure AI Agent Service with the basic setup using API keys authetication for the AI Service/AOAI connection. Agents use multi-tenant search and storage resources fully managed by Microsoft. You won’t have visibility or control over these underlying Azure resources.
This set of templates demonstrates how to set up Azure AI Agent Service with the basic setup using managed identity authetication for the AI Service/AOAI connection. Agents use multi-tenant search and storage resources fully managed by Microsoft. You won’t have visibility or control over these underlying Azure resources.
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.
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.
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.
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.
This set of templates demonstrates how to set up Azure AI Agent Service with virtual network isolation using User Managed Identity authetication for the AI Service/AOAI connection and private network links to connect the agent to your secure data.
This set of templates demonstrates how to set up Azure AI Agent Service with the standard setup, meaning with managed identity authentication for project/hub connections and public internet access enabled. Agents use customer-owned, single-tenant search and storage resources. With this setup, you have full control and visibility over these resources, but you will incur costs based on your usage.
ARM template resource definition
The workspaces resource type can be deployed with operations that target:
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.
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.
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.
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.
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.
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.
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.
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.
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.
This set of templates demonstrates how to set up Azure AI Agent Service with the basic setup using API keys authetication for the AI Service/AOAI connection. Agents use multi-tenant search and storage resources fully managed by Microsoft. You won’t have visibility or control over these underlying Azure resources.
This set of templates demonstrates how to set up Azure AI Agent Service with the basic setup using managed identity authetication for the AI Service/AOAI connection. Agents use multi-tenant search and storage resources fully managed by Microsoft. You won’t have visibility or control over these underlying Azure resources.
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.
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.
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.
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.
This set of templates demonstrates how to set up Azure AI Agent Service with virtual network isolation using User Managed Identity authetication for the AI Service/AOAI connection and private network links to connect the agent to your secure data.
This set of templates demonstrates how to set up Azure AI Agent Service with the standard setup, meaning with managed identity authentication for project/hub connections and public internet access enabled. Agents use customer-owned, single-tenant search and storage resources. With this setup, you have full control and visibility over these resources, but you will incur costs based on your usage.
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.
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.
