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GokuMohandas/Made-With-ML

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cached 2026-03-30T13:00:19.230Z
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GokuMohandas/Made-With-ML

Made-With-ML is a Python-based educational repository for learning how to design, develop, deploy, and iterate on production-grade machine learning applications. It appears actively maintained, with a recent commit on 2026-03-04, and has substantial adoption with 47,026 stars and 7,386 forks.

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Stars47,026
Forks7,386
Default branchmain
Last pushed2026-03-04T23:44:21Z
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Prefer this fork only if you want the older notebook-driven learning experience and the Binder-style accessibility. If you want current production ML, MLOps, deployment, and maintained tooling, upstream is the better choice.

Choose the fork only if you specifically want the older practicalAI-style learning experience or its translated/navigation changes. For most adopters, upstream is the better choice because it is much more current, actively maintained, and aligned with production ML workflows.

Choose this fork if you want a practical, notebook-heavy learning resource with bundled datasets and do not need the latest upstream maintenance. Prefer upstream if you want current MLOps, deployment, and ongoing updates.

Prefer this fork only if you specifically want the older, expanded notebook-and-dataset version of the course. For most adopters, upstream is the better base because it is much newer and more aligned with current production ML workflows.

Choose the fork if you want an older but more guided, lesson-centric ML learning repo with extra deployment/API material. Choose upstream if you want the maintained, current version with fresher tooling and course updates.

Choose the upstream repo unless you specifically need this fork’s namespace or history; it offers no added functionality and is slightly behind upstream.

Choose this fork only if you want a frozen copy to study or customize locally. If you want an actively maintained starting point, upstream is the better choice.

Choose this fork only if you want the older, expanded tutorial experience and do not need current upstream maintenance; for active production-oriented ML learning, upstream is the safer default.

Choose this fork if you want a customized teaching version with extra datasets and lesson notebooks. Choose upstream if you want fresher maintenance, broader project continuity, and the full original workflow surface.

Prefer this fork if you want a beginner-friendly, visually guided learning version of Made-With-ML. Prefer upstream if you want the latest, more complete, actively maintained production-ML course and tooling.