Overview
In ML, code versioning (like Git) is not enough. You also need to know exactly which dataset and which hyperparameters were used to produce a specific model file.
Why it Matters
- Reproducibility: Being able to recreate a model's results exactly.
- Rollback: Quickly switching back to a previous version if a new model performs poorly in production.
- Auditing: Tracking the history of a model for compliance and debugging.
Tools
- DVC (Data Version Control)
- MLflow
- Weights & Biases