1. Event-driven Data Preprocessing for AI with GCP Cloud Functions


    To create an event-driven data preprocessing workflow for AI applications using Google Cloud Platform (GCP), we would typically use Cloud Functions and potentially other GCP products such as Pub/Sub, Cloud Storage, and AI Platform. Here's how this might work at a high level:

    1. A file (presumably containing data that needs preprocessing) is uploaded to a Cloud Storage bucket.
    2. This upload event triggers a Cloud Function, which is set up to respond to such events.
    3. The Cloud Function executes its code, which preprocesses the data as needed.
    4. The processed data may then be published to a Pub/Sub topic if further processing is required, or stored back into Cloud Storage, or directly fed into AI Platform for analysis or machine learning tasks.

    In the Pulumi program below written in Python, we will:

    • Define a Cloud Storage bucket to store our input data.
    • Set up a Cloud Function that triggers when new objects are uploaded to the bucket.
    • Use Cloud Pub/Sub as a messaging service if we need to pass the processed data to other services.
    import pulumi import pulumi_gcp as gcp # Create a GCP Storage Bucket where our data to be processed will be stored data_bucket = gcp.storage.Bucket("data-bucket") # Define the Cloud Function that will be triggered on new data uploads. # This includes defining how the function is triggered, the code it runs, # and required permissions. You'll need to define your own `preprocess_data` # function in Python and upload it to a Cloud Storage bucket, or define inline. preprocess_function = gcp.cloudfunctions.Function("preprocess-function", entry_point="preprocess_data", # The name of the Python function handler runtime="python39", # Specify the runtime and the version source_archive_bucket=data_bucket.name, # The bucket with the source code source_archive_object="path/to/your/deployment.zip", # Path to the zip file in the bucket event_trigger=gcp.cloudfunctions.FunctionEventTriggerArgs( event_type="google.storage.object.finalize", # On file upload event resource=data_bucket.name, ), # We define IAM policy to allow the Cloud Function to interact with other GCP services if needed. labels={"purpose": "preprocess-data"}, ) # Optionally, you can use Pub/Sub to publish processed data for further use, for example: # topic = gcp.pubsub.Topic("processed-data-topic") # Export the Cloud Function URL so that it can be accessed or triggered manually if needed. pulumi.export("function_url", preprocess_function.https_trigger_url)


    • We first import the necessary Pulumi libraries.
    • We create a Cloud Storage bucket called data_bucket where the raw data files will be uploaded.
    • We define a Cloud Function called preprocess_function. Its entry_point is the name of a Python function (preprocess_data) that we must define in our code (not shown here). This function will be responsible for the preprocessing logic that needs to be applied to our data.
    • We specify the Cloud Function's source code using source_archive_bucket and source_archive_object. These would be the location of your uploadable source code, which should be a zip file containing your Python function and any dependencies.
    • event_trigger specifies that our function should trigger on the google.storage.object.finalize event, which occurs when a new object is uploaded to our bucket.
    • Optionally, we can use Pub/Sub (commented out) to create a topic for further data processing needs.

    You'd need to provide the actual preprocessing code in a file, zip this along with any dependencies, and upload it to the specified Cloud Storage bucket. Then, updating the source_archive_object attribute with the correct path to the zip file enables Pulumi to set up the Cloud Function correctly.

    Remember to replace "path/to/your/deployment.zip" with the path to the actual zip file in your bucket containing the Cloud Function code you want to deploy.