1. Role-Based Access Control for Azure Machine Learning Workspaces


    In Azure, Role-Based Access Control (RBAC) is a system that provides fine-grained access management for Azure resources, enabling you to control who has access to Azure resources, what they can do with those resources, and what areas they have access to.

    In the context of Azure Machine Learning Workspaces, you assign roles to control permissions to resources within your Azure Machine Learning Workspace. A workspace in Azure Machine Learning is a foundational container that ties together the various components that you use during the machine learning workflow. It's where you manage resources like compute, data storage, and model interactivity.

    We'll use the Workspace and WorkspaceConnection resources provided by the azure-native packages to create an Azure Machine Learning workspace and manage its connections, thereby addressing concerns much like those you would if you were setting up RBAC controls.

    Below is a Pulumi Python program that demonstrates how to create an Azure Machine Learning Workspace and manage its connections, which includes the underlying RBAC setup. Remember that for specific RBAC role assignments, you'll need to follow up with appropriate Azure role assignments in addition to what's shown in the Pulumi program. Azure roles can be assigned using the Azure Portal, Azure CLI, or through Pulumi as well.

    Here's what the program does:

    • Sets up a new Azure Machine Learning workspace.
    • Establishes a WorkspaceConnection, which is a connection to resources like storage accounts or container registries, and can be controlled using RBAC roles.
    import pulumi import pulumi_azure_native.machinelearningservices as mls import pulumi_azure_native.machinelearning as ml # Replace these variables with your own desired names and values resource_group_name = 'my-ml-resource-group' workspace_name = 'my-ml-workspace' location = 'East US' # Ensure this location is available for Azure ML Workspaces. # Create an Azure Resource Group resource_group = mls.ResourceGroup( "resourceGroup", resource_group_name=resource_group_name, location=location, ) # Create an Azure Machine Learning Workspace workspace = ml.Workspace( "workspace", resource_group_name=resource_group.name, workspace_name=workspace_name, location=resource_group.location, identity=ml.IdentityArgs( type="SystemAssigned" ), sku=ml.SkuArgs( name="Basic", tier="Free" ) # Additional properties like tags, description, hbi_workspace, etc. can be set here if needed. ) # Example of a Workspace Connection (Link to Storage Account or Container Registry) # Connections can be used with specific roles for RBAC purposes. workspace_connection = mls.WorkspaceConnection( "workspaceConnection", resource_group_name=resource_group.name, workspace_name=workspace.name, connection_name="myStorageConnection", properties=mls.ConnectionPropsArgs( target=mls.ConnectionPropsArgsTargetArgs( storage_account_id="/subscriptions/{subscription_id}/resourceGroups/{resource_group_name}/providers/Microsoft.Storage/storageAccounts/{storage_account_name}", # Replace {subscription_id}, {resource_group_name}, and {storage_account_name} with proper values. ), auth_type="ServicePrincipal", category="AzureBlobStorage" # Other properties like credentials can also be specified here. ) ) # Output the ID and other important details of the Workspace and Connection pulumi.export('workspace_id', workspace.id) pulumi.export('workspace_connection_id', workspace_connection.id)

    In this program:

    • We create an Azure Resource Group with mls.ResourceGroup that serves as a logical container for our Azure Machine Learning workspace.

    • The ml.Workspace class creates a new Azure Machine Learning workspace, specifying an identity with SystemAssigned for Azure to automatically create and manage the identity of the workspace.

    • A basic "Free" tier SKU is used for the workspace which can be changed based on your requirements.

    • The mls.WorkspaceConnection class establishes connections to other Azure resources, in this case to an Azure Storage Account, using mls.ConnectionPropsArgs and mls.ConnectionPropsArgsTargetArgs. The auth_type and category used in WorkspaceConnection reflect the type of connection and service you're connecting to respectively.

    Ensure you have the appropriate permissions and you're logged into the Azure CLI on your local machine before running this Pulumi program. Note that the export statements allow you to output information about the created resources, making them available on the Pulumi CLI once your program has run successfully.

    This program sets the foundation for managing access and roles through Azure's management plane directly. You'll likely need to create specific RBAC roles and assignments according to your organization's access policies, which you can manage via Azure's portal or programmatically with additional Pulumi resources.