Hands-On Machine Learning vs 
Made With ML
Hands-On Machine Learning vs Made With ML compared for 2026 — features, license, ease of use, performance and which one to choose. The notebooks of the best-selling ML book vs From notebook to production system.
Updated regularly · curated by OpenSourceAI.tech
Hands-On Machine Learning vs Made With ML at a glance
| Spec | Hands-On Machine Learning | Made With ML |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Book notebooks | Course (MLOps) |
| License | Apache-2.0 | MIT |
| Runs locally | Yes | Yes |
| Primary language | Jupyter | Python |
| Ease of use | Intermediate | Intermediate |
| Best for | the classic path from scikit-learn to deep learning | the gap between a notebook and production |
| GitHub stars | — | 48.7k |
How Hands-On Machine Learning and Made With ML score
| Criterion | Hands-On Machine Learning | Made With ML |
|---|---|---|
| Popularity | n/a | 4.0 |
| Maintenance | n/a | 4.0 |
| Ease of use | 3.5 | 3.5 |
| Privacy | 5.0 | 5.0 |
| License freedom | 5.0 | 5.0 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
Hands-On Machine Learning
Aurélien Géron's companion notebooks: scikit-learn for classical ML, then Keras and TensorFlow for deep learning — the reference practical ML book.
- The most widely used practical ML book
- Every chapter is a runnable notebook
- Covers classical ML properly, not just neural nets
Made With ML
Goku Mohandas' course on taking ML from a notebook to a reliable production system: testing, CI/CD, monitoring, and the engineering most courses ignore.
- Covers the engineering that courses skip
- Testing, CI/CD and monitoring for ML
- Written by a practitioner, not an academic
Key differences
Hands-On Machine Learning is book notebooks, while Made With ML is course (MLOps). Their licenses differ (Apache-2.0 vs MIT), which matters if you ship a commercial product. In short, Hands-On Machine Learning fits the classic path from scikit-learn to deep learning, and Made With ML fits the gap between a notebook and production.
Which should you choose?
Choose Hands-On Machine Learning for the classic path from scikit-learn to deep learning. Choose Made With ML for the gap between a notebook and production.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is Hands-On Machine Learning or Made With ML easier to use?
Both sit at a similar level (Intermediate). Your choice should come down to fit rather than difficulty.
Are Hands-On Machine Learning and Made With ML free?
Hands-On Machine Learning is free and open source (Apache-2.0), and Made With ML is free and open source (MIT). Neither charges for the core software.
Can I run Hands-On Machine Learning and Made With ML locally?
Hands-On Machine Learning: yes · Made With ML: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Hands-On Machine Learning vs Made With ML — which should I pick in 2026?
Choose Hands-On Machine Learning for the classic path from scikit-learn to deep learning. Choose Made With ML for the gap between a notebook and production.
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