Made-With-ML Production ML Course

Key takeaways#

• Made-With-ML is a practical course and codebase for building production-grade machine learning applications.
• The project connects ML modeling work with software engineering, testing, deployment, CI/CD, monitoring, and iteration.
• It is best treated as a learning resource, not a standalone tool.
• The GitHub repository is MIT licensed and has roughly 48k stars and 7.5k forks.

What is Made-With-ML?#

Made-With-ML is a course and reference repository by Goku Mohandas for developers who want to move beyond notebooks and learn how production machine learning systems are built. The project site frames the course around four verbs: design, develop, deploy, and iterate. That structure is useful because most real ML work fails in the gaps between model training, software quality, data operations, serving, and maintenance.

The repository describes its goal plainly: learn how to develop, deploy, and iterate on production-grade ML applications. It uses Python, Jupyter notebooks, PyTorch, Ray, testing, CI/CD, and deployment material to show how an ML project can grow from experimentation into a reliable product workflow.

What the curriculum covers#

The course follows the full ML lifecycle. Learners work through design, data preparation, exploration, preprocessing, training, experiment tracking, hyperparameter tuning, evaluation, serving, scripting, testing, reproducibility, versioning, CI/CD, monitoring, and deployment. That makes it especially useful for developers who already understand basic model concepts but need the engineering practices around them.

The repository is not just slides. It includes notebooks, Python package code, tests, deployment files, documentation, and project configuration. The structure lets readers see how training code, evaluation code, serving code, and operational workflows fit together in one project.

Who should use it#

Made-With-ML is a strong fit for software engineers moving into ML, data scientists who need better production habits, infrastructure engineers supporting model workloads, and technical product leaders who want a grounded view of what production ML requires. It is also useful for recent graduates because it bridges the distance between academic ML exercises and production systems.

Why it matters for builders#

Many AI projects can produce an impressive demo but fail when the data changes, the model needs to be redeployed, or the team has to debug production behavior. Made-With-ML focuses on that operational layer. It teaches the habits that make ML systems repeatable: testable code, documented workflows, experiment tracking, deployable services, and feedback loops.

Source notes#

The official GitHub repository is GokuMohandas/Made-With-ML, and the project site is madewithml.com. GitHub metadata checked during this run showed about 47,954 stars, 7,543 forks, an MIT license, and Jupyter Notebook as the primary language. Because repository metrics change, treat those numbers as a snapshot rather than permanent facts.