1. Event-driven Model Retraining Workflows with GCP Cloud Functions


    To set up an event-driven model retraining workflow using Google Cloud Platform (GCP), we'll use a combination of Cloud Functions, Cloud Pub/Sub, and Cloud Storage. The flow is as follows:

    1. Data Ingestion: Continually ingesting new data to a Cloud Storage bucket, which triggers the workflow.
    2. Event Trigger: When new data arrives in the bucket, an event is generated.
    3. Cloud Function Invocation: This event triggers a Cloud Function through Cloud Pub/Sub.
    4. Model Retraining: Inside the Cloud Function, initiate the model retraining process. This could involve calling another service or running a job on AI Platform, for instance.
    5. Output Storage: Once retraining is complete, store the new model or related artifacts back to Cloud Storage or another appropriate service.

    Here's how to implement this using Pulumi in Python. First, you would define the Cloud Storage bucket for data ingestion, create a Pub/Sub topic to handle the messaging, then set up the Cloud Function that responds to the event and processes the data.

    Below is a program in Python to create the resources necessary for such workflow:

    import pulumi import pulumi_gcp as gcp # Create a Cloud Storage bucket. data_bucket = gcp.storage.Bucket('data-bucket', location='US-CENTRAL1') # Choose your preferred location. # Create a Cloud Pub/Sub topic that will be used to trigger the Cloud Function. pubsub_topic = gcp.pubsub.Topic('model-retrain-topic') # IAM member to grant publish rights to the Cloud Storage bucket on the Pub/Sub topic. pubsub_publisher_role = gcp.storage.BucketIAMMember('bucket-pubsub-publisher', bucket=data_bucket.name, role="roles/storage.objectViewer", member=pulumi.Output.concat("serviceAccount:service-", pubsub_topic.project, "@gcp-sa-pubsub.iam.gserviceaccount.com")) # Setting up a notification configuration for the bucket. # This configuration specifies that any time an object is created in the bucket, it will notify the Pub/Sub topic. bucket_notification = gcp.storage.Notification('bucket-notification', bucket=data_bucket.id, payload_format="JSON_API_V1", topic=pubsub_topic.id) # The Cloud Function that will get triggered on events and handle the model retraining. cloud_function = gcp.cloudfunctions.Function('model-retrain-function', source_archive_bucket=data_bucket.name, runtime='python37', # Replace with the runtime of your choice. available_memory_mb=256, # Adjust the memory allocation based on your needs. source_archive_object='{object_name}.zip', # Replace with the name of your Cloud Function source code archive. trigger_topic=pubsub_topic.name, # Configure the Function to be triggered by messages published to the Pub/Sub topic. entry_point='trigger_model_retrain') # Replace with the entry point function in your source code. # Export the trigger URL of the Cloud Function and the name of the Cloud Storage bucket. pulumi.export('function_trigger_url', cloud_function.https_trigger_url) pulumi.export('data_bucket_name', data_bucket.name)

    This program does the following:

    • Storage Bucket: A Cloud Storage bucket is created to store incoming data for model training.
    • Pub/Sub Topic: A Pub/Sub topic is created which will be used to publish events when new data comes in.
    • IAM Member: Sets up the necessary IAM permissions to allow the Storage bucket to publish events to the Pub/Sub topic.
    • Bucket Notification: Configures Cloud Storage to send notifications to the Pub/Sub topic when new data arrives.
    • Cloud Function: Deploys a Cloud Function that gets triggered by messages on the Pub/Sub topic. This Cloud Function would then be responsible for initiating the model retraining process.

    In order to deploy this program, you would need to package your Cloud Function's source code into a zip archive and upload it to your Cloud Storage bucket, replacing {object_name}.zip with the actual object name of the archive.

    Finally, you will also want to implement the logic inside the Cloud Function (trigger_model_retrain in the example) to handle the event and execute the model retraining procedure.

    Remember to replace placeholders for the source_archive_object and entry_point with actual values from your Cloud Function's code.

    To run the Pulumi program, ensure you have Pulumi installed and configured to use GCP. Then, you can execute pulumi up to create the resources as defined in the program above. Pulumi will display the outputs, such as the Cloud Function trigger URL and the name of the Cloud Storage bucket.