We all know how crucial training data for data scientists is to build quality machine learning models. But when productionizing Machine Learning, Metadata is equally important.
Consider for example:
- Capture of Lineage Information (e.g., Which dataset influences which Model?)
- Capture of Audit Information (e.g, A given model was trained two months ago with the following training/validation performance)
- Reproducible Model Training
- Model Serving Policy (e.g., Which model should be deployed in production based on training statistics)