You recently trained a XGBoost model that you plan to deploy to production for online inference. Before sending a predict request to your model's binary, you need to perform a simple data…

Developing a separate Flask application for preprocessing and a separate model serving implies managing two distinct services or containers, increasing operational complexity and potential cost.

While packaging a Flask app and model into a custom container is a valid approach, utilizing the Vertex AI SDK's `CustomPredictor` class provides a more integrated and optimized solution specifically designed for Vertex AI's managed prediction services.

This option is technically very similar to D, but D is more explicitly clear about packaging *both* the custom predictor and the model into *one* custom container image, which is key for minimizing effort and cost for a single deployment.

Building a custom predictor class based on the Vertex AI SDK and packaging it with the XGBoost model into a custom container image allows both preprocessing and inference logic to run within a single managed service. This approach leverages Vertex AI's infrastructure for online prediction, integrates seamlessly with VPC Service Controls, and minimizes both cost and effort for deployment.