Made With ML vs 
Awesome Machine Learning
Made With ML vs Awesome Machine Learning compared for 2026 — features, license, ease of use, performance and which one to choose. From notebook to production system vs The reference index of ML libraries, by language.
Updated regularly · curated by OpenSourceAI.tech
Made With ML vs Awesome Machine Learning at a glance
| Spec | Made With ML | Awesome Machine Learning |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Course (MLOps) | Curated list |
| License | MIT | CC0-1.0 |
| Runs locally | Yes | Yes |
| Primary language | Python | Markdown |
| Ease of use | Intermediate | Beginner |
| Best for | the gap between a notebook and production | finding the right library in any language |
| GitHub stars | 48.9k | 73.8k |
How Made With ML and Awesome Machine Learning score
| Criterion | Made With ML | Awesome Machine Learning |
|---|---|---|
| Popularity | 4.0 | 4.5 |
| Maintenance | 4.0 | 5.0 |
| Ease of use | 3.5 | 5.0 |
| Privacy | 5.0 | 5.0 |
| License freedom | 5.0 | 3.5 |
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
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
Awesome Machine Learning
The long-standing curated index of machine learning frameworks, libraries and software, organised by programming language — the reference people have used for a decade.
- Maintained for over a decade
- Organised by language, not by hype
- The reference the whole field points to
Key differences
Made With ML is course (MLOps), while Awesome Machine Learning is curated list. Their licenses differ (MIT vs CC0-1.0), which matters if you ship a commercial product. Made With ML leans more intermediate-friendly, whereas Awesome Machine Learning is more suited to beginner users. In short, Made With ML fits the gap between a notebook and production, and Awesome Machine Learning fits finding the right library in any language.
Which should you choose?
Choose Made With ML for the gap between a notebook and production. Choose Awesome Machine Learning for finding the right library in any language.
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 Made With ML or Awesome Machine Learning easier to use?
Awesome Machine Learning is generally the easier of the two to get started with, while Made With ML rewards more setup with more control.
Are Made With ML and Awesome Machine Learning free?
Made With ML is free and open source (MIT), and Awesome Machine Learning is free and open source (CC0-1.0). Neither charges for the core software.
Can I run Made With ML and Awesome Machine Learning locally?
Made With ML: yes · Awesome Machine Learning: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Made With ML vs Awesome Machine Learning — which should I pick in 2026?
Choose Made With ML for the gap between a notebook and production. Choose Awesome Machine Learning for finding the right library in any language.
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