1. Integrate Azure AD with Service Principals for Machine Learning


    To integrate Azure Active Directory (Azure AD) with Service Principals for Azure Machine Learning, we will be performing a few main steps:

    1. Create an Azure AD Application, which represents the identity of the application when it requests access to Azure Machine Learning resources.
    2. Create a Service Principal for the Azure AD Application. A Service Principal is an identity created for use with applications, hosted services, and automated tools to access Azure resources.
    3. Assign the required permissions to the Service Principal.
    4. Create an Azure Machine Learning Workspace with System Assigned Managed Identity.
    5. Grant the Service Principal access to the Azure Machine Learning workspace if necessary.

    First, you need to have the Azure CLI installed and configured with the account having the appropriate permissions to create these resources.

    Here is a Pulumi Python program that does this:

    import pulumi import pulumi_azuread as azuread import pulumi_azure_native.machinelearningservices as ml # Create an Azure AD application ad_app = azuread.Application("machineLearningApp", display_name="machineLearningApp") # Create a Service Principal for the application sp = azuread.ServicePrincipal("machineLearningSp", application_id=ad_app.application_id) # Create an Azure Machine Learning workspace with System Assigned Managed Identity ml_workspace = ml.Workspace("machineLearningWorkspace", resource_group_name='my-resource-group', # Replace 'my-resource-group' with the name of your resource group workspace_name='my-ml-workspace', # Replace 'my-ml-workspace' with the name of your Azure Machine Learning Workspace location='East US', # Replace 'East US' with the Azure region you wish to create your workspace in sku=ml.SkuArgs( name="Basic" # Choose the pricing tier that best fits your needs ), identity=ml.IdentityArgs( type="SystemAssigned" )) # Export the principal ID and Azure Machine Learning Workspace ID pulumi.export('principalId', sp.id) pulumi.export('workspaceId', ml_workspace.id)

    Here's what the above code does:

    • It uses the pulumi_azuread module to create an Azure AD application which represents an identity for your machine learning application.
    • We then create a Service Principal linked to the Azure AD application.
    • The pulumi_azure_native.machinelearningservices module is used to create an Azure Machine Learning workspace with a System Assigned Managed Identity.
    • Lastly, we export the principal ID and workspace ID so we know the identity which we will be giving permissions to and the workspace it will access.

    Please make sure to replace 'my-resource-group', 'my-ml-workspace', and 'East US' with the appropriate values for your environment.

    To further integrate with your resources, you might need to set up role-based access control (RBAC) and grant the Service Principal the necessary permissions to operate. These permissions will depend on your specific requirements like training models, deploying models, or managing the workspace.

    If you'd like to understand more about these resources and why they are being used, you can check the following documentation:

    The actual permission assignment can be done within the Azure portal or programmatically using the Azure SDK or CLI, which is beyond the scope of Pulumi's automation but can technically be included in a Pulumi program using dynamic providers or external program invocations.