1. Filtering GCP Serverless Functions for AI Event-Driven Architecture


    To create a serverless, event-driven architecture on Google Cloud Platform (GCP) that filters and processes events for AI purposes, we will use Google Cloud Functions. Cloud Functions is a fully managed, serverless execution environment for building and connecting cloud services. With Cloud Functions, you upload your code, and the function automatically runs and scales as needed.

    Here's what we will do:

    1. Create a Google Cloud Function using Pulumi.
    2. Write a Python function within this Cloud Function that is triggered by events from a Google Cloud Pub/Sub topic. This topic can receive events that you want your AI system to process.
    3. In the Cloud Function, we will add logic for filtering events. Only the relevant events will be further processed or sent to another service like Google Cloud ML for AI processing.

    Below is a Pulumi program in Python that illustrates how you can set up a simple AI event-driven architecture with Google Cloud Functions and Cloud Pub/Sub.

    import pulumi_gcp as gcp # Set up the Google Cloud Pub/Sub topic. # Events published to this topic can trigger the Cloud Function. topic = gcp.pubsub.Topic('ai-events-topic') # Define the Cloud Function that will process AI events. cloud_function = gcp.cloudfunctions.Function( 'ai-event-processor', # Specify the region where the function will be deployed. region='us-central1', # Define the runtime and the entry point of the function. runtime='python37', entry_point='process_ai_event', # Set up the event trigger using the Pub/Sub topic we created. event_trigger={ 'event_type': 'google.pubsub.topic.publish', 'resource': topic.id, }, # Add environment variables if needed, e.g., to configure thresholds for filtering. environment_variables={ 'THRESHOLD': '0.9', }, ) # This is the actual Python code for the Cloud Function. # It filters events based on their properties, e.g., a confidence score. function_code = """ import base64 import json import os def process_ai_event(event, context): # Threshold for filtering events. threshold = float(os.getenv('THRESHOLD')) # Decode the Pub/Sub message data. message_data = base64.b64decode(event['data']).decode('utf-8') # Convert the JSON string to a Python dictionary. message_dict = json.loads(message_data) # Example filtering based on a 'confidence' score in the event data. confidence_score = message_dict.get('confidence', 0) if confidence_score >= threshold: # Process the event further or send it to another service, such as AI Platform. print(f'Processing event with confidence score: {confidence_score}') else: # Ignore events that don't meet the threshold. print(f'Ignoring event with confidence score: {confidence_score}') """ # Upload the Python code to Google Cloud Storage and deploy it as a source of the Cloud Function. bucket = gcp.storage.Bucket('cloud-function-source-bucket') source_archive_object = gcp.storage.BucketObject( 'function-code-zip', bucket=bucket.name, source=pulumi.AssetArchive({ '.': pulumi.FileArchive('/path_to_your_function_directory') # Replace with the path to your function code. }), ) # Update the Cloud Function to reference the uploaded code. cloud_function.source_archive_bucket = bucket.name cloud_function.source_archive_object = source_archive_object.name # Export the Cloud Function URL and the Pub/Sub topic name to use them elsewhere if needed. pulumi.export('function_url', cloud_function.https_trigger_url) pulumi.export('topic_name', topic.name)

    This Pulumi program does several things:

    • It defines a Cloud Pub/Sub topic to which events can be sent.
    • It sets up a Google Cloud Function with an event trigger linked to the Pub/Sub topic.
    • It includes a skeleton of the Python function code that should be deployed. The code processes the incoming Pub/Sub messages and filters out events based on a threshold.

    Note: The process_ai_event Python function is provided as an inline example. In practice, you would write this code in a separate .py file, package it with any dependencies, and upload it to a Google Cloud Storage bucket which the Cloud Function will use as its source. Make sure to adapt the path in FileArchive to point to the directory containing your function code.