1. Real-Time AI Predictive Analytics with GCP Pub/Sub


    To build a real-time AI predictive analytics system on Google Cloud Platform (GCP) using Pub/Sub, you'll need to set up a few key resources:

    1. Google Cloud Pub/Sub: A messaging service that allows you to send and receive messages between independent applications.
    2. Google Cloud Functions: A serverless execution environment for building and connecting cloud services. In this scenario, it's used to process messages from Pub/Sub and perform predictive analytics.
    3. Google AI Platform: Provides a suite of machine learning products that enables developers to build and deploy AI models.

    In this program, we'll create a Pub/Sub topic to receive data. We'll then set up a Cloud Function that gets triggered by messages on this topic, processes the data by invoking a deployed AI model on the AI Platform, and perhaps publishes the result to another Pub/Sub topic or stores it in a database.

    Below is an example Pulumi program in Python that sets up this architecture:

    import pulumi import pulumi_gcp as gcp # Create a Pub/Sub topic that will receive input data for predictions input_topic = gcp.pubsub.Topic("input_topic") # IAM roles for the Pub/Sub subscription to allow the Cloud Function to be triggered by topic messages invoker_role = gcp.projects.IAMMember("invoker_role", member=f"serviceAccount:{input_topic.id}@appspot.gserviceaccount.com", role="roles/pubsub.subscriber", project=input_topic.project) # A Cloud Function triggered by the Pub/Sub topic predictive_function = gcp.cloudfunctions.Function("predictive_function", entry_point="predict", # assuming 'predict' function exists in the source code runtime="python39", source_archive_bucket=gcp.storage.Bucket("source_bucket").name, source_archive_object=gcp.storage.BucketObject("zip", bucket="source_bucket", source=pulumi.FileArchive("./function_source_code.zip") # a zip file with the Cloud Function source code ).name, event_trigger=gcp.cloudfunctions.FunctionEventTriggerArgs( event_type="providers/cloud.pubsub/eventTypes/topic.publish", resource=input_topic.name)) # Optionally, create another Pub/Sub topic for the output results output_topic = gcp.pubsub.Topic("output_topic") # IAM roles for the Cloud Function to allow publishing to the output Pub/Sub topic publisher_role = gcp.projects.IAMMember("publisher_role", member=f"serviceAccount:{predictive_function.service_account_email}", role="roles/pubsub.publisher", project=output_topic.project) # Export the URLs for invoking the Cloud Function & the name of the output topic pulumi.export("input_topic", input_topic.name) pulumi.export("output_topic", output_topic.name) pulumi.export("function_url", predictive_function.https_trigger_url)

    In this Pulumi program:

    • input_topic is the Pub/Sub topic where data for predictions will be sent.
    • predictive_function is the Google Cloud Function that's triggered when messages are published to input_topic. It should contain the logic to call the AI Platform with the received data for making predictions.
    • The publisher_role and invoker_role set up appropriate permissions for the Cloud Function to be triggered and to publish messages.
    • output_topic is created to publish the predictions. The Cloud Function would have to publish to this topic.

    Before running this code, make sure you've:

    1. Enabled the Cloud Functions and Pub/Sub API in your GCP project.
    2. Set up the Pulumi GCP provider by logging in with gcp.configure() or setting environment variables.
    3. Prepared a function_source_code.zip file containing the Python code for your Cloud Function. The predict function inside the code should handle incoming Pub/Sub messages and use the AI Platform to make predictions.

    This example covers the infrastructure setup. The application logic within the Cloud Function should be written to handle incoming messages and interact with other GCP services according to your predictive analytics requirements.

    Remember to replace placeholder code like ./function_source_code.zip with actual file paths relevant to your project.