Reproducibility
← Back to Model Lifecycle
Ensuring ML experiments can be exactly reproduced. Requires fixing random seeds, pinning environment/library versions, and versioning data (DVC). Without reproducibility, ML development becomes unreliable and debugging is impossible.
Related
- Experiment Tracking (records run configuration)
- Data Versioning (version the data too)