What tests should a training pipeline have?
mediumAnswer
- (1) Data validation: schema + ranges + expected volume.
- (2) Feature parity: features computed in training match serve-time features.
- (3) Model quality: metric on frozen validation ≥ baseline (regression guard).
- (4) Fairness: per-group metrics acceptable.
- (5) Latency budget: model p99 < X ms on test hardware.
- (6) Model size: < Y MB (for edge / mobile).
- (7) Smoke test: end-to-end pipeline runs on tiny sample.
- All in CI blocking merge / release.
Check yourself — multiple choice
- Only accuracy
- Data validation + feature parity + quality regression + fairness + latency budget + size limit + smoke test; all CI blocking
- Random
- Not needed
Training pipeline tests: data + parity + quality + fairness + latency + size + smoke.
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