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.