Exam Professional Machine Learning Engineer topic 1 question 226 discussion

Option B is simpler (Flask app handles preproc directly) and less costly (Storage within the container) *** Storing the pickled model in Cloud Storage adds network calls during prediction, increasing latency and cost. ***The XGBoost Predictor (C & D) task adds unneeded complexity to a simple preprocessing task

why not c? While it utilizes the XGBoost Predictor, packaging the pickled model in the container increases image size and requires redeploying the container for model updates.

My Answer: D This option involves using the Vertex AI SDK to build a custom predictor class, which allows for easy integration with the XGBoost model. Packaging the handler in a custom container image based on a Vertex built-in container image ensures compatibility and smooth deployment. Storing the pickled model in Cloud Storage provides a scalable and reliable way to access the model. Deploying the model to Vertex AI Endpoints allows for easy management and scaling of inference requests, while minimizing cost and effort. The main difference between C and D is where the model is saved. So, is a good practice to save models in GCS because Separation of Concerns, Flexibility, and Reduced Image Size

Minimal Custom Code: Leverages the pre-built XGBoost Predictor class for core model prediction, reducing development effort and potential errors. Optimized Container Image: Utilizes a Vertex built-in container image, pre-configured for efficient model serving and compatibility with Vertex AI Endpoints. Separated Model Storage: Stores the model in Cloud Storage, reducing container image size and simplifying model updates independently of the container. VPC Service Controls: Vertex AI Endpoints support VPC Service Controls, ensuring adherence to internal traffic restrictions.