1. AutoML Models Accessibility via GCP Endpoints


    To make AutoML models accessible via Google Cloud Platform (GCP) Endpoints, we will create a Google Cloud Endpoint service. Google Cloud Endpoints is a system that helps you create, deploy, and manage APIs on Google Cloud. When working with machine learning models such as those created using AutoML, deploying an API endpoint allows you to serve predictions from your trained models over the network.

    In this program, we're going to use Pulumi with the GCP provider to create an Endpoint service that will serve an AutoML model. The following Pulumi program automates this process:

    1. It defines a new Endpoint service (gcp.endpoints.Service).
    2. It configures IAM policies for the service if needed, using resources like gcp.endpoints.ServiceIamPolicy or gcp.endpoints.ServiceIamMember.
    3. It deploys the AutoML model behind this endpoint for accessible predictions.

    Here is the detailed Pulumi program in Python:

    import pulumi import pulumi_gcp as gcp # Replace these variables with your project-specific information. project = 'my-project-id' model_name = 'my-automl-model-name' # The name of the deployed AutoML model service_name = f"{project}.appspot.com" # Your service domain, typically in the form [PROJECT_ID].appspot.com # Create a Google Cloud Endpoint service that points to the AutoML model. endpoint_service = gcp.endpoints.Service( "my-automl-endpoint-service", serviceName=service_name, project=project, openapiConfig=model_name) # A YAML or JSON OpenAPI specification of the API # Optionally configure an IAM policy for the service if you need to control access to it. # This example sets the policy to allow public access, be sure to change this according to your requirements. public_policy = gcp.endpoints.ServiceIamPolicy( "my-automl-endpoint-service-public-policy", serviceName=service_name, policyData='''{ "bindings": [{ "role": "roles/servicemanagement.serviceConsumer", "members": ["allUsers"] }] }''' ) # Use this URL to make prediction requests to your AutoML model. # The Endpoint URL will be a combination of the service name and a base path for AutoML predictions. automl_url = pulumi.Output.concat("https://", service_name, "/v1/models/", model_name, ":predict") # Export the URL so that we can access it later. pulumi.export('AutoML Model Endpoint URL', automl_url)

    In this code, we have:

    • Defined a gcp.endpoints.Service to create an Endpoint service.
    • Configured the OpenAPI specification to point to our AutoML model using the openapiConfig attribute.
    • Set a public IAM policy on the Endpoint service to allow all users to access it. This is for demonstration purposes, and in a production environment, you would want to restrict access according to your own security guidelines.
    • Exported the URL that you can use to send prediction requests to your AutoML model.

    When you run this Pulumi program with pulumi up, Pulumi will provision the necessary GCP resources and output the URL you can use to interact with your AutoML model. From there, you'll be able to make HTTP requests to this URL to get predictions based on the model you've trained.