1. Staging Environments for AI Workload Rollouts on Azure.


    When you're setting up staging environments for AI workload rollouts on Azure, you'll be looking to create an infrastructure that allows you to deploy, test, and validate your AI models before they're rolled out into production. Azure provides a comprehensive set of services that cater to machine learning workloads which can be used to set up your staging environment.

    Here's a rundown of the Azure services and Pulumi resources you might use for such a setup:

    1. Azure Machine Learning (AML) Workspace: This is the foundational service for machine learning on Azure. It provides the space where you can experiment, train, and deploy machine learning models. In Pulumi, you'd use the Workspace resource to create this.

    2. AML Environments: You can define reusable environments for training and deployment, which include the necessary dependencies for your models. This makes it easier to manage versions of your dependencies and libraries. In Pulumi, this corresponds to EnvironmentSpecificationVersion.

    3. AML Online Endpoint: This is where you'd deploy your models for real-time inference, and it's where your models would serve predictions once moved into production. In the staging phase, you might deploy models here to test their behavior in a production-like setting. The corresponding Pulumi resource is OnlineDeployment.

    4. Managed Compute Instances: These are scalable compute resources that can be used for training and inference. They are managed by Azure, which means you spend less time on infrastructure management. You could use BatchDeployment in Pulumi for batch processing workloads.

    5. Networking and Access Control: Properly set up networking ensures that your staging environment is secure and isolated from production. You can use Pulumi resources like Network and Subnet to define your network topology.

    In the example below, we’ll create an Azure Machine Learning Workspace and an Environment Specification Version for your AI workload staging using Pulumi and Python.

    import pulumi import pulumi_azure_native as azure_native # Create an Azure resource group for organizing resources resource_group = azure_native.resources.ResourceGroup("ai_resource_group") # Create an Azure Machine Learning Workspace ai_workspace = azure_native.machinelearning.Workspace("ai_workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.machinelearning.SkuArgs( name="Standard", # Choose a SKU that fits your needs (Basic, Enterprise, etc.) ), description="Staging environment for AI workloads", friendly_name="ai_staging_workspace" ) # Define an environment specification for machine learning workloads ai_environment_spec = azure_native.machinelearningservices.EnvironmentSpecificationVersion("ai_environment_spec", resource_group_name=resource_group.name, workspace_name=ai_workspace.name, location=resource_group.location, properties=azure_native.machinelearningservices.EnvironmentSpecificationVersionPropertiesArgs( conda_file="env_dependencies.yml", # Path to your conda environment file inference_container_properties=azure_native.machinelearningservices.InferenceContainerPropertiesArgs( scoring_route=azure_native.machinelearningservices.RouteArgs( path="/score", port=5001 ), liveness_route=azure_native.machinelearningservices.RouteArgs( path="/liveness", port=5001 ), readiness_route=azure_native.machinelearningservices.RouteArgs( path="/readiness", port=5001 ), ), description="ML environment for staging", docker=azure_native.machinelearningservices.DockerArgs( base_image="mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04" # Use an appropriate base image ) ) ) # Export outputs to view in the Pulumi console or CLI pulumi.export("workspace_url", ai_workspace.workspace_url) pulumi.export("environment_spec_name", ai_environment_spec.name)

    This program, when executed with Pulumi, will set up the foundation for a staging environment on Azure. Specifically, it creates an AI workspace within a new resource group. It also defines an environment specification with settings for scoring, liveness, and readiness endpoints, a container image, and conda file dependencies. It sets the stage for deploying machine learning models in an isolated environment that closely mirrors production, allowing you to test everything thoroughly before going live.

    To run this Pulumi program, you need to have the Pulumi CLI installed and Azure credentials configured. You would save the code into a file with a .py extension and run the following commands in your terminal:

    pulumi up # To preview and deploy the infrastructure

    After deployment, you can inspect the outputs, which will include URLs to access the created resources. Ensure you clean up resources after testing so as not to incur unwanted costs:

    pulumi destroy # To tear down all deployed resources