PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #190
PROFESSIONAL-MACHINE-LEARNING-ENGINEER Question #190: Real Exam Question with Answer & Explanation
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Question
You recently used XGBoost to train a model in Python that will be used for online serving. Your model prediction service will be called by a backend service implemented in Golang running on a Google Kubernetes Engine (GKE) cluster. Your model requires pre and postprocessing steps. You need to implement the processing steps so that they run at serving time. You want to minimize code changes and infrastructure maintenance, and deploy your model into production as quickly as possible. What should you do?
Options
- AUse FastAPI to implement an HTTP server. Create a Docker image that runs your HTTP server,
- BUse FastAPI to implement an HTTP server. Create a Docker image that runs your HTTP server,
- CUse the Predictor interface to implement a custom prediction routine. Build the custom container,
- DUse the XGBoost prebuilt serving container when importing the trained model into Vertex AI.
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