1. Automated AI Model Retraining Workflows using GCP Cloud Functions


    Automating AI model retraining workflows involves several components working together within Google Cloud Platform (GCP). Typically, this would involve setting up a Cloud Function that responds to certain triggers (like changes in a data store), retraining the machine learning model using Google Cloud AI Platform, and deploying the new model version for predictions. We’ll need to set up resources such as Cloud Storage for storing data, Cloud Functions for the retraining logic, AI Platform for training and hosting the model, and possibly Cloud Scheduler or Pub/Sub to trigger the whole process.

    In the following Pulumi program, we will create a simplified version of this workflow. It will include a Cloud Function that is triggered via HTTP, simulating a manual trigger of the retraining process. For the sake of this example, we will not include the actual model training code or deployment. In a real-world scenario, you would embed that logic within the Cloud Function or call out to the respective AI Platform APIs.

    import pulumi import pulumi_gcp as gcp # Define a Google Cloud Storage Bucket where our training data will reside. data_bucket = gcp.storage.Bucket('data-bucket') # Define a Google Cloud Function. # This function is written inline as an example, but you should deploy an actual file for production usage. ai_retrain_function = gcp.cloudfunctions.Function('ai-retrain-func', entry_point='retrain_model', # The name of the function in your source code runtime='python37', # The runtime for the function trigger_http=True, # This function triggers on HTTP requests source_archive_bucket=data_bucket.name, source_archive_object='source.zip', # Archive file that contains the source code for the function environment_variables={ 'DATASET_BUCKET': data_bucket.name, # You could add more configuration such as the model name, version, etc. } ) # Setting up IAM policy to allow invocation of the function. ai_retrain_function_iam = gcp.cloudfunctions.FunctionIamMember('ai-retrain-func-iam', project=ai_retrain_function.project, region=ai_retrain_function.region, cloud_function=ai_retrain_function.name, role='roles/cloudfunctions.invoker', member='allUsers' # WARNING: This is for illustration only and NOT recommended for production. ) # Export the Cloud Function URL so we can easily access it pulumi.export('function_url', ai_retrain_function.https_trigger_url)


    • Cloud Storage Bucket: We create a gcp.storage.Bucket which acts as the storage location for our training data that the machine learning model will use for retraining.

    • Cloud Function: We then set up a gcp.cloudfunctions.Function that will contain the logic for retraining our AI model. In this example, the function is configured to trigger via HTTP, allowing you to easily trigger retraining with an HTTP request. The actual code for the AI model retraining would need to be provided by you, packaged into a zip file (source.zip), and uploaded to the data-bucket bucket. You would replace 'source.zip' with the actual path to your zip file with the source code.

    • IAM Policy: The gcp.cloudfunctions.FunctionIamMember resource sets up the necessary IAM policy so that the Cloud Function can be invoked. In this example, allUsers are allowed to invoke the function; however, in a production system, you would restrict this to only trusted entities.

    • Export: Lastly, we export the HTTPS trigger URL of the Cloud Function so that once the program is deployed, you know how to trigger the Cloud Function.

    This example assumes that you have defined the Cloud Function code to handle the retraining logic and packaging it into source.zip stored in the Cloud Storage bucket. In a full production system, you would likely also incorporate Cloud Scheduler or Pub/Sub to automize the trigger based on specific schedules or events, and integrate Cloud AI Platform for the actual training and serving of the model.