1. PubSub as Event-Driven Architecture for AI Workflows


    Event-driven architecture is a design pattern where applications are structured around the production, detection, consumption, and reaction to events. In the case of cloud platforms like Google Cloud Platform (GCP), Pub/Sub is a fully managed messaging service that allows you to decouple event producers from event consumers. This asynchronous messaging service enables you to send and receive messages between independent applications.

    Here is how you can use Pub/Sub within an AI workflow:

    1. Event Production: An event producer (e.g., an application that processes data) publishes a message to a Pub/Sub topic whenever a significant action occurs (like new data is available for processing, a model needs to be trained, etc.).

    2. Message Management: Pub/Sub stores the message in the topic until it can be delivered to all subscribers.

    3. Event Subscription: Event consumers (e.g., services that kick off AI workflows) subscribe to the topic and receive messages. This could involve triggering a machine learning model training job, running a batch prediction, or re-training a model with new data.

    4. Event Reaction: Upon receiving a message, the subscribing service can process the message accordingly and potentially publish new events as a result.

    Let's put this into code using Pulumi and the GCP SDK for Python. The example will set up a Pub/Sub topic, a subscription for that topic, and a dummy AI workflow represented as a Cloud Function that gets triggered by a message on the Pub/Sub topic.

    import pulumi import pulumi_gcp as gcp # Create a Pub/Sub topic that will be used to publish events. topic = gcp.pubsub.Topic("ai-workflow-topic") # Create a subscription to the topic. Messages published to the topic will be # delivered to subscribers. subscription = gcp.pubsub.Subscription("ai-workflow-subscription", topic=topic.name, ack_deadline_seconds=20) # Dummy Cloud Function that represents the AI workflow event handler (consumer). # In an actual use case, this Cloud Function would kick off an AI-related job # like training a model, making predictions, etc., when a message is published # to the Pub/Sub topic. ai_workflow_handler = gcp.cloudfunctions.Function("aiWorkflowHandler", source_archive_bucket=gcp.storage.Bucket("source-bucket").name, runtime="python37", entry_point="handler", trigger_http=True, environment_variables={ "PUBSUB_TOPIC": topic.name, }) # A Cloud Function trigger that listens for messages on the Pub/Sub topic. # This causes the function to execute whenever a message is published. trigger = gcp.cloudfunctions.FunctionIamMember("trigger", project=ai_workflow_handler.project, region=ai_workflow_handler.region, cloud_function=ai_workflow_handler.name, role="roles/pubsub.subscriber", member=pulumi.Output.concat("serviceAccount:", ai_workflow_handler.service_account_email)) # Create a Pub/Sub topic message to simulate the publishing of an event. # This is a way to test the setup. pubsub_message = gcp.pubsub.TopicMessage("test-message", topic=topic.name, data="dGVzdCBtZXNzYWdl") # "test message" base64 encoded # Export the URL of the trigger HTTP endpoint from the Cloud Function. pulumi.export("ai_workflow_handler_url", ai_workflow_handler.https_trigger_url)

    This program does the following:

    • Creates a new Pub/Sub topic named ai-workflow-topic.
    • Creates a subscription to this topic with the name ai-workflow-subscription.
    • Sets up a dummy Google Cloud Function named aiWorkflowHandler. This function is supposed to represent your AI workflow event handler that would process messages.
    • Creates an IAM policy that grants the Cloud Function service account the roles/pubsub.subscriber role, allowing it to consume messages from the Pub/Sub topic.
    • Sends a test message to the topic to demonstrate a simple event publication. In production, you'd have your applications publish messages to the topic.
    • Exports the Cloud Function trigger URL as an output. In an actual use case, services could invoke the Cloud Function directly via this URL.

    Note: For this code to work, you'll need to replace the source_archive_bucket property with a reference to an actual Cloud Storage bucket where your function's source code resides. Also, you would substitute the entry_point with the name of the function in your source code that should be called when the Cloud Function is triggered.

    Remember, before running this code, you need to set up Pulumi with the appropriate permissions and configurations to deploy resources in GCP and have the GCP SDK and Pulumi packages installed for Python.