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Turn the path into reviewable evidence with its scoped project milestones and explicit grading criteria.
Pick a public dataset and write a clean, reproducible notebook: explore the data (distributions, missingness, leakage risks), state a dumb baseline,...
Pick a real tabular dataset: build a leakage-safe sklearn Pipeline (split → preprocess → CV → held-out test), beat a stated baseline, track every run...
Serve a trained model behind a FastAPI endpoint: schema-validated input/output, train/serve-shared preprocessing, version pinned from a registry,...