1. AI Workload Orchestration with Databricks Workspace


    In order to orchestrate AI workloads, a Databricks Workspace is an essential resource. It provides a unified platform for data engineering, collaborative data science, full lifecycle machine learning, and business analytics through a lakehouse architecture. Databricks on Azure is a first-party service on Azure, designed to allow users to easily build and deploy machine learning models.

    Below, I will guide you through creating an Azure Databricks Workspace using Pulumi with Python. We'll use the azure-native provider, which is a newer provider for Pulumi that exposes the Azure API directly, allowing you to have access to the full breadth of Azure services.

    Here's how we will approach the task:

    • Define a resource group: This will serve as a container that holds related resources for the Azure Databricks Workspace.
    • Create the Databricks Workspace: We will specify the necessary properties, such as the location (the Azure region where the workspace will be deployed) and the SKU (which controls the pricing tier).

    Let's jump into the code:

    import pulumi import pulumi_azure_native as azure_native # Create an Azure resource group where we will place the Databricks workspace resource_group = azure_native.resources.ResourceGroup("databricks_resource_group") # Create the Azure Databricks Workspace databricks_workspace = azure_native.databricks.Workspace("databricks_workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.databricks.SkuArgs( name="standard" # You can choose between "standard", "premium", or other available SKUs ) ) # Output the Databricks Workspace URL to access it later pulumi.export("databricks_workspace_url", databricks_workspace.workspace_url)

    In this program:

    • We import the necessary Pulumi libraries for Azure.
    • We create an instance of ResourceGroup from the azure_native.resources module to organize the Azure resources.
    • We define the Databricks Workspace using the azure_native.databricks.Workspace class. The sku property specifies the SKU for the Workspace; in our case, the "standard" tier is chosen.
    • At the end of our Pulumi program, we export the Databricks Workspace URL, which you can use to access the Databricks Workspace through your web browser.

    You can run this Pulumi program by saving the code in a file (e.g., __main__.py) and executing pulumi up in the terminal from the same directory as your file. This will provision the resources as per the program's definition in your Azure Subscription.

    Note: Ensure that you have already set up Pulumi with the appropriate Azure credentials. You can find more details on how to configure Pulumi for Azure here.

    Remember, you will be billed by Azure for the resources you provision using this Pulumi program, so keep an eye on your Cloud spend and resources, and clean up resources that you no longer need by running pulumi destroy.