1. Trigger-based ML Model Deployment with GCP Cloud Functions


    To accomplish the task of deploying an ML model using Google Cloud Functions triggered by a specific event (such as a file upload to Google Cloud Storage), we will use a few Google Cloud services and resources. Here's how you can approach this:

    1. Google Cloud Storage (GCS): This is where our ML model artifacts will be stored, such as the trained model files. We'll create a GCS bucket for this purpose.

    2. Google Cloud Functions (GCF): We will write a function that gets executed in response to our trigger event. This function will handle the deployment process of the ML model.

    3. Cloud Scheduler or Pub/Sub: To periodically check for the trigger or to directly integrate a trigger, we'll use Cloud Scheduler for time-based triggers or Pub/Sub for event-driven triggers.

    4. Google ML Engine or AI Platform: The actual deployment of the ML model will be on Google ML Engine (or AI Platform), which allows us to serve the ML model over an API for predictions.

    The following Pulumi program in Python sets up this infrastructure:

    1. A GCS bucket is created to store the ML model artifacts.
    2. A GCF is created that is triggered by the upload of new artifacts to the GCS bucket.
    3. An IAM role is assigned to the GCF to have access to deploy models in ML Engine.

    Let's write a Pulumi program to accomplish this:

    import pulumi import pulumi_gcp as gcp # Replace with your actual project and region if necessary project = 'your-gcp-project' region = 'us-central1' # Create a Google Cloud Storage bucket to store ML model artifacts ml_bucket = gcp.storage.Bucket("ml-model-bucket") # Define the Google Cloud Function that will be triggered to deploy the model deploy_ml_model_function = gcp.cloudfunctions.Function("deploy-ml-model-function", entry_point="deploy_model", # The name of the function in your code runtime="python39", # Adjust to match the runtime of your function region=region, source_archive_bucket=ml_bucket.name, source_archive_object="path/to/your/cloud-function.zip", # Path to the zipped source code trigger_http=True, # This creates an HTTP-triggered function; see docs for bucket-triggered options event_trigger={ "event_type": "google.storage.object.finalize", # Event type for new object creation "resource": ml_bucket.id, # The bucket id to watch for the trigger }) # IAM role binding allowing the Cloud Function's service account to deploy models in AI Platform gcf_service_account = deploy_ml_model_function.service_account_email model_deployment_iam_binding = gcp.projects.IAMMember("model-deployment-iam-binding", role="roles/ml.developer", # Role allowing deployment of ML models; adjust the role as needed member=pulumi.Output.concat("serviceAccount:", gcf_service_account)) pulumi.export("trigger_http_url", deploy_ml_model_function.https_trigger_url) pulumi.export("ml_bucket_name", ml_bucket.name)

    Before running this program, you will need the following prerequisites:

    • Make sure you have the Pulumi CLI installed.
    • Make sure you have Google Cloud SDK (gcloud) installed and have authenticated with your GCP account.
    • Ensure you have the necessary permissions in your GCP project to create these resources.
    • Write the Cloud Function code to deploy the ML model and store it in path/to/your/cloud-function.zip.

    This Pulumi program will provision the necessary infrastructure for your trigger-based ML model deployment using Google Cloud Functions. The pulumi.export commands output the HTTP trigger URL and the bucket name, which can be useful for further configurations or debugging.

    If you wish to use this setup, make sure to replace placeholders like your-gcp-project with your own values and write the Cloud Function's code that will handle the model deployment logic.