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MLOps · Intermediate · 6 h

Serving a model as an API

Save a trained pipeline with its metadata, validate inputs, serve predictions with FastAPI and test the service.

Why employers care: A model nobody can call has no value. Interviews ask how you would deploy a model and what could go wrong.

In the roadmaps for: Data Scientist, AI/ML Engineer

Lessons, in order

  1. Serve a model with FastAPI, and test itSave a trained pipeline with metadata, validate every request, and put predictions behind a small, tested HTTP service.4 min

Next shelf: Monitoring, drift and retraining