Made With ML

The course goes beyond model building to cover the operational concerns that determine whether ML projects succeed: data management, experiment tracking, testing, CI/CD for ML, and monitoring.

The curriculum is structured around a production ML project that learners build end-to-end: defining ML tasks properly, designing data pipelines with proper validation, training and evaluating models with rigorous methodology, packaging models for serving, implementing inference APIs, setting up CI/CD pipelines for automated testing and deployment, and establishing monitoring for model and data drift.

This project-centric approach means learners exit with a deployable system rather than disconnected notebooks.

ML engineers early in their careers who have learned model building but lack production engineering exposure, software engineers transitioning into ML who need to develop data science skills alongside their existing engineering background, and data scientists who have shipped models to production but want to formalize their practices use Made With ML to close the gap between research-oriented ML education and the engineering rigor that production systems require.

The resource has attracted significant adoptionhundreds of thousands of learnersreflecting how underserved this production-engineering perspective has been in traditional ML education.