1. Event-Driven Machine Learning Model Scoring


    Creating event-driven architectures to perform Machine Learning (ML) model scoring can leverage different cloud services that enable serverless computing, message queuing, and ML model hosting. A typical workflow involves:

    1. An event source such as an IoT device sending data, an application performing actions, or a file landing on object storage.
    2. This event triggers a serverless function which acts whenever new data appears, performs operations, or reacts to user actions.
    3. The serverless function then invokes an ML model hosted either on a managed ML service or a custom deployment.
    4. Finally, the ML model processes the input and returns a scoring result, which can be sent back to the serverless function or written to a datastore.

    Let's construct a program with Pulumi in Python to implement such flow on Google Cloud Platform (GCP). We'll use the following GCP services:

    • Cloud Functions: Responds to events from various Google Cloud sources.
    • Pub/Sub: Serves as an intermediary message queue that decouples event producers from consumers.
    • AI Platform: Hosts and serves ML models.

    First, we set up a Google Cloud Function that is triggered by an event, say, a message on a Pub/Sub topic. Then, the function interacts with an ML model hosted on the AI Platform to score the data in the event.

    Here is a Pulumi program that sets up such an architecture:

    import pulumi import pulumi_gcp as gcp # Your Google Cloud project and region project = 'your-gcp-project' region = 'your-gcp-region' # Create a Pub/Sub topic to receive events input_topic = gcp.pubsub.Topic("input-topic") # Define the Cloud Function # This function triggers on messages from the Pub/Sub topic ml_scoring_function = gcp.cloudfunctions.Function("ml-scoring-function", source_archive_bucket=gcp.storage.Bucket("bucket").name, runtime="python38", source_archive_object=gcp.storage.BucketObject("source-archive-object", bucket=bucket.name, source=pulumi.FileAsset("path/to/your/zip/file/containing/function/code.zip") ).name, entry_point="handler", # replace 'handler' with the name of the function you'd like to execute event_trigger=gcp.cloudfunctions.FunctionEventTriggerArgs( event_type="google.pubsub.topic.publish", resource=input_topic.name, ), ) # Create an AI Platform prediction model that will be used for scoring # REPLACE 'your-model-name' with your actual ML model name on the AI Platform ai_model = gcp.ml.EngineModel("ai-model", name="your-model-name", regions=[region], project=project, # Make sure your model has a default version, or specify one explicitly here ) # Ensure the Cloud Function depends on the ML model being available - optional step based on your use case pulumi.Resource("dependency", opts=pulumi.ResourceOptions(depends_on=[ai_model])) # Export the Cloud Function URL and the AI Platform model details for reference pulumi.export("function_url", ml_scoring_function.https_trigger_url) pulumi.export("ai_model_name", ai_model.name)

    Let's break down what we've done in the above script:

    1. Set up a Pub/Sub topic: The topic input-topic acts as a central hub for receiving events/data which will trigger the scoring process.

    2. Cloud Function: We define a Google Cloud Function ml-scoring-function that's triggered by a message arriving at our Pub/Sub topic. The function code needs to be packaged in a ZIP file and uploaded to a Google Cloud Storage bucket, which is then referenced by the Cloud Function. The entry_point parameter is the name of the handler function within your code that will be running. When creating a real implementation, replace the path/to/... placeholder with the actual path to your zip file.

    3. AI Platform Model: The ai_model is an instance of a machine learning model deployed on GCP's AI Platform. This model will be used to score the incoming data. Ensure that the model is already trained, deployed, and has a default version set up in the AI Platform before running the Pulumi code. Adjust your-model-name to your actual model name.

    4. Exported Outputs: After the deployment, Pulumi provides us with useful outputs such as the HTTPS trigger URL of the Cloud Function and the AI Platform model name for further reference or integration with other services or tools.

    Be sure the code handlers for the Cloud Function are properly written to call the AI Platform scoring service, handle the events, and process the scoring. This Pulumi program provides the foundation for the infrastructure, while the actual data processing and ML scoring logic will reside within your Cloud Function's code.