provenance tracking

**Provenance Tracking** for ML models is the **systematic recording of a model's complete history** — from training data, through all training runs, hyperparameter choices, code versions, and deployment stages, providing a full audit trail of how the model was created and modified. **Provenance Components** - **Data Provenance**: Which datasets, versions, preprocessing steps, and labels were used. - **Training Provenance**: Hyperparameters, random seeds, training code version, compute resources. - **Model Provenance**: Model architecture, weight checkpoints, evaluation metrics at each stage. - **Deployment Provenance**: When deployed, which version, what configuration, serving infrastructure. **Why It Matters** - **Reproducibility**: Full provenance enables exact reproduction of any model version. - **Auditing**: Regulatory compliance requires demonstrating how models were built and validated. - **Debugging**: When a model fails, provenance helps trace the failure back to its root cause. **Provenance Tracking** is **the model's complete biography** — recording every decision and data point that shaped the model from creation to deployment.