1. Scalable Inference Endpoint for Machine Learning Models


    To deploy a scalable inference endpoint for machine learning models, we will use Azure Machine Learning Services, specifically the ServerlessEndpoint resource provided by Pulumi's azure-native package. This allows us to deploy and manage our machine learning model without having to provision or manage the underlying infrastructure actively.

    Here's a step-by-step guide to setting up a machine learning inference endpoint:

    1. Create an Azure Resource Group: An Azure resource group is a container that holds related resources for an Azure solution. We'll start by creating a resource group for our machine learning services.

    2. Create an Azure Machine Learning Workspace: An Azure Machine Learning Workspace is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models.

    3. Deploy a Serverless Endpoint: The serverless endpoint hosts your deployed machine learning model in a scalable and managed environment, which scales on-demand based on the incoming requests without the need to manage any compute resources.

    Here's the Pulumi program written in Python that accomplishes this:

    import pulumi import pulumi_azure_native.resources as resources import pulumi_azure_native.machinelearningservices as aml # Step 1: Create an Azure Resource Group for our Machine Learning Services resource_group = resources.ResourceGroup("resource_group") # Step 2: Create an Azure Machine Learning Workspace ml_workspace = aml.Workspace( "ml_workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=aml.SkuArgs(name="Basic"), description="A workspace for Azure Machine Learning", ) # Step 3: Deploy a Serverless Endpoint to host the machine learning model. # Here, the model itself, the environment configuration, and inference configuration # would need to be defined as per the user's ML model requirements. serverless_endpoint = aml.ServerlessEndpoint( "serverless_endpoint", resource_group_name=resource_group.name, workspace_name=ml_workspace.name, serverless_endpoint_properties=aml.ServerlessEndpointPropertiesArgs( # The model, environment, and inference configuration go here # For simplicity, we're omitting those details and assuming they have been defined # in the Azure machine learning workspace. # Example: offer_name="Your offer name", publisher="Model publisher" ), location=resource_group.location, # Additional properties can be set according to requirements, such as: # auth_mode, capacity_reservation, description, etc. ) # Export the endpoint URL so it can be accessed. pulumi.export("endpoint_url", pulumi.Output.concat("http://", serverless_endpoint.name, ".azurewebsites.net"))

    In this program:

    • The ResourceGroup resource creates a new resource group in your Azure subscription.
    • The Workspace resource sets up an Azure Machine Learning workspace, where 'Basic' is specified as the SKU. You can change it to suit your pricing tier and features.
    • The ServerlessEndpoint resource deploys a new serverless endpoint, which is the core part of creating an inference endpoint. Note that specific properties related to the machine learning model deployment are required, such as the model itself, the environment for the model (dependencies, runtime, etc.), and the inference configuration (like entry scripts).

    To use this program:

    1. Ensure you have the Pulumi CLI installed and configured for Azure.
    2. Save the above code as __main__.py in a new Pulumi project directory.
    3. Inside the directory, run pulumi up from the command line. This command will create the resources in Azure as defined by your Pulumi program.

    Remember to replace the placeholder comments with your actual machine learning model, environment, and inference configuration details for a real-world deployment.