#28 · Primary category: Education & Research

Made-With-ML

data-engineering data-quality data-science deep-learning distributed-ml distributed-training llms machine-learning mlops natural-language-processing python pytorch ray

Learn how to develop, deploy and iterate on production-grade ML applications.

Project last updated:03/04/26

GitHub Stars

49.3K

Forks

7.7K

Contributors

1

License

MIT

Why we included this project

Made With ML is a free, project-based course that walks you through the entire machine learning lifecycle, starting from a Jupyter notebook experiment and ending with a deployed, monitored service. It's not a library you drop into your stack; the payoff comes from following the lessons and adapting the code to your own project. For developers and small teams trying to understand how data pipelines, training, tuning, serving, and CI/CD fit together, it offers a realistic blueprint you can actually follow. The material also spends time on MLOps details like testing, logging, and versioning, which are easy to overlook when you learn ML in isolation. If you're ready to move past toy models and ship something real, this is a practical map of the territory.

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