1. Scalable Event-Driven ML Workflows with GCP Cloud Tasks


    Creating an event-driven machine learning (ML) workflow in Google Cloud Platform (GCP) involves several components:

    1. Cloud Tasks: This service allows you to manage the execution, dispatch, and delivery of a large number of distributed tasks. For an ML workflow, you might use Cloud Tasks to schedule data preprocessing jobs, trigger ML model training, or initiate batch predictions.

    2. Workflows: The Workflows service in GCP orchestrates and automates Google Cloud or HTTP-based API services with serverless workflows. Workflows can coordinate tasks in an ML pipeline, such as data extraction, transformation, model evaluation, and more.

    3. Dataflow: Google Cloud Dataflow is a fully managed service for stream and batch processing. In an ML context, Dataflow can handle large-scale data processing tasks that prepare data for training or inference.

    4. ML services: GCP offers a range of ML products such as AI Platform for training and deploying ML models. You can use these services to run custom model training jobs, serve predictions, and manage your ML models.

    The Pulumi Python program below sets up an event-driven ML workflow using Google Cloud Tasks, Workflows, and other services to orchestrate a scalable ML pipeline:

    import pulumi import pulumi_gcp as gcp # Set up a Cloud Tasks queue to manage tasks cloud_tasks_queue = gcp.cloudtasks.Queue("ml-workflow-queue", location="us-central1", ) # Using GCP Workflows to orchestrate the tasks ml_workflow = gcp.workflows.Workflow("ml-workflow", location="us-central1", description="Workflow to manage ML tasks", source_contents=""" # Define the workflow steps here # e.g., preprocess data, train model, perform predictions - initialize: call: http.get args: url: YOUR_INIT_ENDPOINT - preprocess: call: http.get args: url: YOUR_PREPROCESS_ENDPOINT - trainModel: call: http.get args: url: YOUR_TRAIN_ENDPOINT - predict: call: http.get args: url: YOUR_PREDICTION_ENDPOINT - cleanup: call: http.get args: url: YOUR_CLEANUP_ENDPOINT """, ) # Optionally, you can use Dataflow for data processing before training # Assuming `YOUR_TEMPLATE_PATH` is a path to a Dataflow template dataflow_job = gcp.dataflow.Job("ml-dataflow-job", temp_gcs_location="gs://your-bucket/temp-location", # Replace with your GCS bucket template_gcs_path="YOUR_TEMPLATE_PATH", ) # This is just a skeleton for setting up an ML workflow with GCP and Pulumi. # Each task in the workflow or Dataflow job would correspond to some part of # the ML workload, such as data preparation or model training. # Export the URLs to access the created services pulumi.export('cloud_tasks_queue_name', cloud_tasks_queue.name) pulumi.export('workflow_name', ml_workflow.name) pulumi.export('dataflow_job_name', dataflow_job.name)

    This program sets up a Cloud Tasks queue to manage and dispatch tasks, a GCP workflow to orchestrate various steps such as data preprocessing, model training, and predictions, and an optional Dataflow job for robust data processing.

    Please remember to replace placeholders like YOUR_INIT_ENDPOINT, YOUR_PREPROCESS_ENDPOINT, YOUR_TRAIN_ENDPOINT, YOUR_PREDICTION_ENDPOINT, YOUR_CLEANUP_ENDPOINT, YOUR_TEMPLATE_PATH, and the GCS bucket locations with actual values pertaining to your application. Each step in the workflow (e.g., initialize, preprocess, trainModel, predict, cleanup) should correspond to actual endpoints that trigger specific parts of your ML pipeline. You would also need to deploy your code that would run on these endpoints, such as a containerized application, to handle the ML tasks.

    The pulumi.export statements output the names of the created resources, which can be useful for debugging or for integrating with other systems.