Mle Workflow

A comprehensive framework for taking ML work from notebooks to production with explicit data contracts, reproducible training, and operational monitoring. Covers the full lifecycle: product framing, metric design, baseline models, feature engineering, evaluation gates, deployment, and rollback paths. The real value is in the MLE-to-SWE translation table that maps ranking systems, classifiers, and forecasting pipelines onto existing code review, testing, and deployment patterns instead of inventing parallel infrastructure. Best for teams hardening existing models or planning production ML features where you need reviewable artifacts and clear promotion criteria. Overkill for exploratory work, but that's the point.