1. Public Connectivity for Azure ML Workspace Interface


    In order to set up an Azure Machine Learning Workspace with public connectivity, you will need to use the Azure Machine Learning services provided by Pulumi. The steps include creating an Azure Machine Learning Workspace resource with public network access enabled. This workspace is the foundational pillar of Azure Machine Learning, providing a centralized place for your ML training scripts, datasets, and models.

    Here is how you can create an Azure ML Workspace with Pulumi in Python:

    1. Import necessary modules: You will need to import the Pulumi Azure Native SDK to interact with Azure resources.

    2. Create a Resource Group: Typically, you start with a Resource Group that acts as a container for your Azure resources.

    3. Create the Machine Learning Workspace: Define the workspace resource, ensuring that the public_network_access attribute is set to allow connectivity.

    4. Exporting Outputs: Optionally, you can export any output such as the Azure Machine Learning Workspace URL.

    Let's go through the Pulumi code to achieve this:

    import pulumi import pulumi_azure_native as azure_native # Create a new resource group resource_group = azure_native.resources.ResourceGroup('resource_group') # Create an Azure Machine Learning Workspace with public connectivity ml_workspace = azure_native.machinelearningservices.Workspace( 'ml_workspace', resource_group_name=resource_group.name, location=resource_group.location, identity=azure_native.machinelearningservices.IdentityArgs( type="SystemAssigned" ), sku=azure_native.machinelearningservices.SkuArgs( name="Basic", # Depending on your needs, you might choose a different SKU ), public_network_access="Enabled", # This enables public connectivity tags={ "Environment": "PublicTest" } ) # Export the Azure ML Workspace URL pulumi.export('workspace_url', ml_workspace.discovery_url)

    In the code above:

    • We start with importing the required Pulumi module to interact with Azure (pulumi_azure_native).
    • Then, we establish a new resource group to contain our Azure resources.
    • Next, we define the Azure Machine Learning Workspace. Notice the public_network_access is set to "Enabled". This is important as it allows the workspace to be accessed over a public network.
    • Finally, we export the discovery URL of the ML workspace as an output. This URL can be used to interact with the workspace through the Azure portal or other tools.

    You can run this Pulumi program using the Pulumi CLI. First, you would save the code to a file, typically named __main__.py. Then, assuming you have Azure credentials configured and the Pulumi CLI installed, you can deploy the resources using the following commands:

    pulumi up

    This command will provision the resources as defined. Ensure you review the plan that Pulumi outputs before confirming the deployment.

    Once deployed, you'll have a working Azure Machine Learning Workspace with public connectivity, ready for you to use for ML experiments and model training. Remember to manage your public endpoints carefully and enforce security best practices to protect your resources.