1. Managed Machine Learning Model Rollouts and A/B Testing


    To manage machine learning model rollouts and A/B testing, you will typically need a combination of services that allow for model hosting, versioning, and traffic routing to different model versions. For instance, in the context of Azure, you can leverage Azure Machine Learning Service which provides a workspace resource for machine learning workflows, including model management and A/B testing.

    Below is a Pulumi Python program that demonstrates how you could set up an Azure environment for managing your machine learning models. In this example, we'll focus on setting up an Azure Machine Learning Workspace, which is a foundational piece for organizing and managing all the assets related to your machine learning models. You can use workspaces to group your machine learning models and associated resources, and a workspace is required if you want to train and deploy models using Azure Machine Learning.

    Here's how you can create an Azure Machine Learning Workspace with Pulumi in Python:

    import pulumi from pulumi_azure_native import machinelearning as azure_ml # Define the resource group where the machine learning workspace will reside. resource_group = azure_ml.ResourceGroup("resource_group", location="East US") # Define the Azure Machine Learning Workspace. # Documentation: https://www.pulumi.com/registry/packages/azure-native/api-docs/machinelearning/workspace/ ml_workspace = azure_ml.Workspace("ml_workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_ml.SkuArgs(name="Basic"), description="AML Workspace for model rollouts and A/B testing") # Export the Azure Machine Learning Workspace URL, which can be used to access the workspace from the Azure portal. pulumi.export('ml_workspace_url', ml_workspace.web_url)

    In the above code:

    • We start by defining a resource group, which is a container for resources in Azure.
    • Then we create an instance of Workspace, which represents an Azure Machine Learning workspace. Note that we're using a basic SKU here, but Azure ML offers other SKUs that may better suit your needs.
    • We're also passing in resource_group.name as the name of the resource group for the workspace, and resource_group.location as the geographic location to host the workspace.
    • Finally, we export the web URL of the workspace, which allows you to view it in the Azure portal.

    Once your workspace is up and running, you can use the Azure Machine Learning services to:

    • Register machine learning models.
    • Deploy models as web services for real-time and batch scoring.
    • Set up A/B testing of models to compare performance across different versions.

    Additionally, Azure offers features to track experiments, manage datasets, and more—all of which can be part of a robust MLOps (Machine Learning Operations) setup.

    Remember, this code is an initial setup. A/B testing and model version management would likely involve further configurations and resources, such as deploying multiple model versions and setting up traffic routing for A/B testing. These advanced setups depend on your specific A/B testing strategy and will require additional logic within the Azure ML workspace and potentially other Azure services.