1. Secure Model Deployment via GCP Endpoints


    To securely deploy a model using Google Cloud Platform (GCP) Endpoints, you will typically need to create a service endpoint that will serve the model's predictions. Google Cloud Endpoints are a tool that allows developers to monitor, manage, and scale APIs. With Endpoints, you gain insight into the API usage patterns and can protect your API with various authentication mechanisms.

    In this case, the model could be served using AI Platform Predictions or you might have a custom service that you want to deploy. Either way, you need to create a GCP Endpoints Service, which acts as a gateway for your clients to send requests to your model.

    Here's a hypothetical secure deployment that achieves the following:

    1. Create a GCP Endpoints Service to define your API's name and behavior.
    2. Deploy your API implementation, which can be a Docker container running on Google Kubernetes Engine or a managed service like AI Platform Predictions.
    3. Implement access control using IAM policies to ensure only authenticated requests can access your endpoints.

    Below is a Pulumi program written in Python that does the following:

    • Define a GCP Endpoints Service, describing its configuration.
    • Implement IAM policies on the service to secure it, allowing only certain authenticated users or service accounts to invoke the service.
    import pulumi import pulumi_gcp as gcp # Define a GCP Endpoints Service. # The specification for the service can be provided as an OpenAPI spec # with `openapi_config` or as a gRPC service config with `grpc_config`. # For this example, let's assume an OpenAPI specification. # Remember to replace `YOUR_SERVICE_NAME` with your actual service domain. service = gcp.endpoints.Service("my-model-service", service_name="YOUR_SERVICE_NAME", # Unique identifier for your service project="your-gcp-project-id", openapi_config=pulumi.FileAsset("openapi.yaml")) # Implement IAM Policy on the service to control access. # Replace `your-gcp-project-id` and `service-account@email.com` # with your actual GCP project ID and the email of your service account. iam_policy = gcp.endpoints.ServiceIamPolicy("my-model-service-iam-policy", service_name=service.service_name, policy_data=pulumi.Output.all(service.service_name).apply(lambda args: json.dumps({ "bindings": [{ "role": "roles/endpoints.portalUser", # Role defining permissions "members": [ "serviceAccount:service-account@email.com", # This should be the service account email ], }], }))) pulumi.export("service_name", service.service_name) pulumi.export("iam_policy", iam_policy.policy_data)

    In this program, we are defining a GCP Endpoints Service. You would need to replace YOUR_SERVICE_NAME with the appropriate service name for your deployment, and provide a configuration file named openapi.yaml representing your API's design. The OpenAPI specification defines how your API looks like, including the available endpoints, request formats, and response structures.

    Then, we create a ServiceIamPolicy to set the IAM policy for the service, which defines who has the permission to do what on this service. In the example, the role roles/endpoints.portalUser is granted to a particular service account, restricting the use of the service to this account. You would need to use the actual email address of your service account.

    After deploying this program with Pulumi, your service will be provisioned with your specified configuration and with access restricted as per the IAM policy. The pulumi.export lines output the service name and IAM policy data for your reference after deployment.

    Remember that prior to running this code, you will need to have the openapi.yaml file ready and ensure that you have the proper permissions to create services and IAM policies in your GCP project.