Expose a trained model as a simple web service
Practical MLOps knowledge from model serialization to serving. Package the model into a standard format, containerize it, expose a REST endpoint behind a load balancer, and add monitoring. A bare Flask server without containers or health checks is a red flag.
This tests whether you understand the MLOps serving lifecycle beyond writing a predict function, including serialization and production reliability. Serialize the model into a format like ONNX or TorchScript, then build a container image bundling the runtime and dependencies. Deploy it behind an API gateway and load balancer across workers, exposing a monitoring endpoint for latency and drift. Proposing a raw Flask app with model.load and app.run while ignoring containers and monitoring.
Read the original → axelmendoza.com
- #mlops
- #model-serving
- #system-design
- #deployment
- #production
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