1. AI-driven Content Moderation Pipelines with GCP Cloud Functions


    Content moderation is crucial for maintaining the quality of user-generated content on any platform. To create a content moderation pipeline using Google Cloud Functions, you would typically set up a GCP Cloud Function to be triggered by new content submissions, process the content to detect any inappropriate material using machine learning or pre-defined rules, and then take appropriate action, such as flagging, removing, or approving the content.

    The GCP Cloud Function would interact with other GCP services such as Google Cloud Pub/Sub for messaging, Google Cloud Storage for storing content, and possibly Google Cloud Machine Learning APIs for content analysis. We're going to set up a simple GCP Cloud Function that could act as a part of an AI-driven content moderation pipeline.

    Let's write a program in Python using Pulumi to define the infrastructure for such a pipeline:

    1. We will use the google-native:cloudfunctions/v1:function resource to create a new Cloud Function.
    2. The function will be triggered by new files uploaded to a specific Cloud Storage bucket.
    3. We will configure the Cloud Function with the appropriate runtime and entry point for our application logic.

    Here's what the complete Pulumi program would look like:

    import pulumi import pulumi_gcp as gcp # Set up a Google Cloud Storage Bucket where the content to be moderated will be stored. content_bucket = gcp.storage.Bucket("content-bucket") # IAM policy to allow the Cloud Function to access the Cloud Storage bucket. bucket_iam_binding = gcp.storage.BucketIAMBinding("bucket-iam-binding", bucket=content_bucket.name, role="roles/storage.objectViewer", members=["allUsers"]) # Please update the members as per your requirement for security reasons # Set up a Google Cloud Function that gets triggered on new file uploads to the bucket. moderation_function = gcp.cloudfunctions.Function("moderation-function", runtime="python39", # The runtime environment for the Cloud Function entry_point="moderate_content", # The name of the function inside your code available_memory_mb=256, # Adjust as needed source_archive_bucket=content_bucket.id, source_archive_object=pulumi.AssetArchive({ # Your source code archive '.': pulumi.FileArchive('./content-moderation') # Local path to Cloud Function source }), trigger_http=False, # This function is triggered by GCS events, not HTTP event_trigger=gcp.cloudfunctions.FunctionEventTriggerArgs( event_type="google.storage.object.finalize", # The event type for new file uploads resource=content_bucket.name, # The bucket to attach the trigger to ) ) # Outputs the URL that triggers the function - useful for HTTP-triggered functions # Not applicable here as we use a direct GCS trigger, but provided for reference pulumi.export("content_moderation_function_url", moderation_function.https_trigger_url)

    In this program:

    • We create a Google Cloud Storage bucket named content-bucket to store user-generated content that needs moderation.
    • We apply an IAM policy to this bucket that allows the Cloud Function to access objects. The IAM role roles/storage.objectViewer grants read access to the bucket's content. For the members list, we've set allUsers, which might not be secure for actual production use. It is recommended to restrict this access to the specific service accounts or groups that need it.
    • A Cloud Function called moderation-function is created that listens to the finalize event of the Cloud Storage, which triggers whenever a new file is created in the bucket.
    • The source_archive_object contains a zipped archive of the Python application code that contains content moderation logic, which you would replace with the path to your actual application code.

    Note: This code assumes you have the Cloud Function code in the ./content-moderation directory, and this code should include a function called moderate_content that handles the event.

    To run this program, you would:

    1. Install Pulumi and configure GCP credentials.
    2. Place your Cloud Function code in the specified directory.
    3. Use pulumi up to launch the infrastructure.

    This setup creates the infrastructural backbone for an AI-driven content moderation pipeline. Depending on your needs, you could expand this with more advanced AI services for content analysis, such as the Google Cloud Video Intelligence API for video content or the Cloud Natural Language API for text content.