mle-workflow

To turn ML notebook experiments into reproducible production pipelines, codify data contracts, establish versioned artifacts, and formalize evaluation gates to ensure consistent, auditable training runs.

Data contracts in machine learning workflows are formal agreements defining entities, features, and labels. They ensure reproducibility and governance by standardizing inputs across training, evaluation, and serving stages.

You add evaluation gates and rollback plans for ML deployment by defining promotion criteria based on model performance metrics, establishing safety thresholds, and packaging artifacts with automated deployment reversal triggers.

Yes, you can use this workflow for ranking systems, recommender pipelines, embeddings, and forecasting models. It applies across planning, building, reviewing, and monitoring to satisfy production requirements.

The best way to set up monitoring dashboards for ML models is to package them for serving with integrated monitoring hooks that track observable product outcomes and model performance across the entire lifecycle.

Your production ML workflow lacks governance and auditability because ad-hoc scripts and notebooks often bypass formal data contracts, versioned artifacts, and reproducible training pipelines required for robust auditing.