1. AI Model Training with Azure Machine Learning Workspaces


    To set up an Azure Machine Learning Workspace for AI model training using Pulumi, we'll define a Pulumi program in Python. This workspace is a foundational piece in the Azure Machine Learning service that provides a space where you can experiment, train, and deploy your machine learning models.

    Here are the steps we'll follow in the program:

    1. Create a resource group in Azure to organize all the resources.
    2. Set up the Azure Machine Learning Workspace within this resource group.

    Below, I'll present to you a Pulumi program in Python that accomplishes this. I'll include comments in the code to help you understand each part of the program.

    import pulumi import pulumi_azure_native as azure_native # Create an Azure resource group to organize our resources resource_group = azure_native.resources.ResourceGroup("ai_resource_group") # Create the Azure Machine Learning Workspace within the resource group ml_workspace = azure_native.machinelearningservices.Workspace( "ai_ml_workspace", resource_group_name=resource_group.name, location=resource_group.location, identity=azure_native.machinelearningservices.WorkspaceIdentityArgs( type="SystemAssigned" ), sku=azure_native.machinelearningservices.SkuArgs( name="Basic", ), # Owner email, user storage account are hypothetical and should be replaced with actual values workspace_name="my-ml-workspace", owner_email="owner@example.com", user_storage_account_id="/subscriptions/{subscription-id}/resourceGroups/{resource-group-name}/providers/Microsoft.Storage/storageAccounts/{storage-account-name}" ) # Export the Azure Machine Learning Workspace URL so you can access it easily pulumi.export("ml_workspace_url", ml_workspace.workspace_url)

    In this code, we're using two major Azure resources from the azure-native provider:

    • Resource Group (ResourceGroup): This resource is declared to hold related resources for an Azure solution. By organizing resources in a resource group, you can manage them together. We generate a new resource group named ai_resource_group.

    • Machine Learning Workspace (Workspace): This resource is the main place where you'll manage your machine learning tasks. We create an ML workspace in the resource group we've just created. It uses "Basic" SKU which is typically an entry-level tier for experimenting with Azure Machine Learning. We need to assign an identity (in this case, a system-assigned managed identity), supply a unique workspace name, and specify storage information which will be utilized by the ML workspace.

    The pulumi.export statement at the end provides a way to output the URL of the workspace which you can use to access the workspace in the Azure portal.

    To run this Pulumi program, you'll need to have Pulumi installed on your machine, have an Azure account configured with the Azure CLI, and set up your Pulumi stack to use Azure. Once set up, you can simply run pulumi up to see the preview of the changes, and confirm to deploy them to Azure.

    Remember to replace placeholders like owner@example.com and storage account information with actual values that you have on your Azure subscription.

    Each resource creation in Pulumi is represented by a class that takes several parameters related to that Azure resource. For example, when we create Workspace, we need to specify the location, identity, SKU, and other attributes to configure it properly. The types of these parameters are strongly-typed Python classes that come with the azure-native Pulumi package, which helps with auto-completion and type checking.

    Feel free to explore and add other resources or configurations as needed for your specific use case, such as attaching compute resources, setting up datastores, or integrating with other services.