How do you make ML experiments reproducible?
mediumAnswer
- Pin seeds and library versions, use deterministic ops when available, containerize the environment (Docker), version code (git), data (DVC/lakeFS), and models (MLflow / model registry).
- Log all hyperparameters and metrics per run.
- Store the exact commit + data snapshot that produced any model in production.
- Cache intermediate artifacts.
Check yourself — multiple choice
- Only code versioning is required
- Reproducibility needs code, data, environment, and seeds versioned together
- Data versioning is unnecessary
- Containers make reproducibility worse
Code + data + environment + seeds all must be captured.
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