Tooling: Experiment Tracking and Metadata Management: Implementing systems to log model parameters, metrics, code versions, and data sources for reproducibility
Reproducibility is one of the most practical quality standards in machine learning. If a team cannot explain how a model was trained, which data version was used, and what configuration produced a specific result, then the model is hard to trust and risky to deploy. This is where experiment tracking and metadata management become essential….