1. REST API Endpoints for Model Inference Services


    To create REST API endpoints for model inference services, you would typically need a managed cloud service that allows you to deploy machine learning models and expose them as REST APIs. For the purpose of this explanation, we will use Azure as our cloud provider and utilize Azure Machine Learning Service, specifically the Serverless Endpoint resource which supports this use case.

    Azure Machine Learning Service offers an end-to-end machine learning platform where you can train, deploy, automate, manage, and track ML models. It provides serverless endpoints for deploying models as web services that can be consumed through REST API endpoints.

    Here's a high-level overview of how you would do this using Pulumi with Python:

    1. Setup Azure Machine Learning Workspace: Before you can deploy any models, you'll need to create a workspace that is the foundational block in the Azure Machine Learning service that provides a space where you can work with your machine learning tasks.
    2. Register a Model: Train your model and register it within your Azure ML Workspace.
    3. Create a Serverless Endpoint: Serverless endpoints in Azure ML allow you to deploy your models without having to manage the underlying compute.
    4. Deploy a Model to the Endpoint: Deploy your registered model to the serverless endpoint.
    5. Consume the Endpoint: Once deployed, your model is accessible via a REST API endpoint that you can use for inference.

    The Pulumi resource used for this scenario is ServerlessEndpoint from the Azure Machine Learning Services.

    Let's assume you already have a machine learning model ready to be deployed. The following Pulumi program demonstrates how to set up a serverless endpoint:

    import pulumi import pulumi_azure_native.machinelearningservices as ml # Configure Azure resource group resource_group = ml.ResourceGroup("resource_group", location="eastus") # Configure Azure Machine Learning workspace workspace_name = "my_ml_workspace" workspace = ml.MachineLearningWorkspace("workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=ml.SKUArgs( name="Standard" ), # Add other workspace configurations as needed ) # Assuming the model is already registered in the Azure ML workspace, we can deploy it # We need to include the model name and version in the deployment configuration model_name = "my_model" model_version = "1" # Configure a serverless endpoint for model inference serverless_endpoint = ml.ServerlessEndpoint("serverless_endpoint", resource_group_name=resource_group.name, workspace_name=workspace.name, location=resource_group.location, serverless_endpoint_properties=ml.ServerlessEndpointPropertiesArgs( offer=ml.OfferArgs( offer_name=model_name, publisher="your_publisher_name", # Replace with your actual publisher name ), # The auth mode and other configurations would be set here ), # Include tags or other configurations as needed ) # Output the REST API URL for the serverless inference endpoint pulumi.export("endpoint_url", serverless_endpoint.endpoint_uri)

    The code above demonstrates how to set up a machine learning workspace, create a serverless endpoint, and deploy a model. To deploy a real model, you would need additional steps:

    • Training the model either outside of Azure ML and registering the model in Azure ML workspace, or by using Azure ML pipelines for the end-to-end model training and deployment.
    • Configuring the scoring script that includes the model inference logic and loading of the model.
    • Configuring compute resources if necessary (though serverless endpoints abstract this away).
    • Setting up authentication and other security measures for your endpoint.

    This program assumes that you have an Azure subscription and the necessary permissions to create resources within it. It also assumes that Pulumi is set up and configured to interact with your Azure account.

    Please note that this code is a starting point and does not include actual model deployment nor the required security measures for production use.