1. Real-time Machine Learning Prediction Stream with GCP Pub/Sub


    Creating a real-time machine learning prediction stream using Google Cloud Platform's (GCP) Pub/Sub service with Pulumi involves several steps. The setup includes creating a topic for publishing messages, a subscription to receive messages, and integrating the ML model for predictions. In this context, we'll use Pulumi's GCP provider to create these resources.

    Here's a high-level overview of the steps involved, followed by a Pulumi Python program to implement them:

    1. Set up a Pub/Sub topic: This serves as a channel for sending messages that contain data for making predictions.
    2. Create a subscription for the topic: This allows a service (such as your machine learning model) to listen for messages published to the topic and process them for predictions.
    3. Register a Google Cloud ML Engine Model: You need a pre-trained machine learning model hosted on Google Cloud ML Engine for making predictions.
    4. Set up a listener for the subscription: When a message is published to the topic, the subscription captures it, and the listener invokes the ML model to perform a prediction using the message's data.
    5. Process the prediction results: Once a prediction is made, the result can be stored, returned to the sender, or passed to another service for further action.

    Let's start writing the Pulumi program to create these resources:

    import pulumi import pulumi_gcp as gcp # Step 1: Create a Pub/Sub Topic # This is where messages will be published. Each message can contain data # like features that will be sent to the machine learning model for prediction. ml_prediction_topic = gcp.pubsub.Topic("ml-prediction-topic") # Step 2: Create a subscription to the topic # The subscription will manage and deliver the messages to a designated endpoint, # such as a Cloud Function or a server, to trigger the prediction process. ml_prediction_subscription = gcp.pubsub.Subscription("ml-prediction-subscription", topic=ml_prediction_topic.name, ack_deadline_seconds=20, # Customizable: The time in which a message needs to be acknowledged. push_config=gcp.pubsub.SubscriptionPushConfigArgs( # Specify an endpoint that will trigger the ML model for performing predictions. push_endpoint="https://<your-cloud-function-or-service-url>/predict" ) ) # Step 3: Register the ML model # Assuming you have a pre-trained model, you register it with GCP ML Engine. # Note: You should replace the 'name', 'description', and 'default_version' attributes with your actual model's details. ml_model = gcp.ml.EngineModel("ml-model", name="<your-model-name>", description="A machine learning model for real-time predictions", default_version=gcp.ml.EngineModelDefaultVersionArgs( name="<your-model-version-name>", ), # Optional: Enable request-response logging for predictions. online_prediction_logging=True ) # This program sets up the infrastructure required for the prediction stream. However, # the actual implementation of invoking the ML model when a message is received, and # processing the prediction results, typically involves additional code, such as a Cloud Function # or a server application that's listening to the subscription's endpoint mentioned above. pulumi.export("pubsub_topic_name", ml_prediction_topic.name) pulumi.export("pubsub_subscription_name", ml_prediction_subscription.name) pulumi.export("ml_model_name", ml_model.name)

    This program creates the necessary Pub/Sub infrastructure to start working on a real-time ML prediction stream. It:

    • Defines a pubsub.Topic that acts as a channel for messages to be sent for prediction.
    • Configures a pubsub.Subscription which sets how these messages will reach the ML model.
    • Registers an ML model using ml.EngineModel, which assumes you have a trained model ready to be used for making predictions.

    Keep in mind that you will need to replace placeholder values such as "<your-cloud-function-or-service-url>", "<your-model-name>", and "<your-model-version-name>" with your specific details related to the ML model and endpoint.

    Also note that the endpoint of the subscription has been set to https://<your-cloud-function-or-service-url>/predict, assuming that you have a service running that receives HTTP POST requests and invokes your machine learning model. In a real-world scenario, this could be a Google Cloud Function or an application running on a server, a GKE cluster, or even a serverless compute environment like Cloud Run.

    Once messages are published to the topic, they will be pushed to this endpoint, which should handle the business logic of interacting with your ML model, passing the data for prediction, and handling the response.

    For more information on using Pulumi to define GCP resources, you can refer to: