CI/CD

A practical guide to data drift monitoring and CI/CD for production ML systems: how to detect model degradation, data drift, and concept drift, and how to ship changes safely with CI/CD.

A practical guide to feature stores, experiment tracking, and model registry for Data Scientists: what each component does, when to use them, and how they connect in a production ML system.

A five-part guide to data storage and processing for production ML systems: relational, column-oriented, vector, and key-value databases, plus streaming and batch processing explained for Data Scientists.

A five-part introduction guide to Production ML Systems for Data Scientists: data storage, ML pipelines, experiment tracking, monitoring, and CI/CD.

Complete Template of Real-World ML Project Structure