Applied ML vs 
Awesome Machine Learning
Applied ML vs Awesome Machine Learning compared for 2026 — features, license, ease of use, performance and which one to choose. How real companies actually ship ML vs The reference index of ML libraries, by language.
Updated regularly · curated by olud.ai
Applied ML vs Awesome Machine Learning at a glance
| Spec | Applied ML | Awesome Machine Learning |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Curated papers & posts | Curated list |
| License | MIT | CC0-1.0 |
| Runs locally | Yes | Yes |
| Primary language | Markdown | Markdown |
| Ease of use | Intermediate | Beginner |
| Best for | learning from what companies really did | finding the right library in any language |
| GitHub stars | 29.9k | 73.6k |
How Applied ML and Awesome Machine Learning score
| Criterion | Applied ML | Awesome Machine Learning |
|---|---|---|
| Popularity | 3.5 | 4.5 |
| Maintenance | 2.0 | 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
Applied ML
Eugene Yan's curated collection of papers and engineering blog posts on how companies actually build and deploy ML systems in production — organised by problem, not by algorithm.
- Real production systems, not toy examples
- Organised by problem, not by algorithm
- Curated by a practising ML engineer
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
Applied ML is curated papers & posts, while Awesome Machine Learning is curated list. Their licenses differ (MIT vs CC0-1.0), which matters if you ship a commercial product. Applied ML leans more intermediate-friendly, whereas Awesome Machine Learning is more suited to beginner users. In short, Applied ML fits learning from what companies really did, and Awesome Machine Learning fits finding the right library in any language.
Which should you choose?
Choose Applied ML for learning from what companies really did. 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 Applied ML or Awesome Machine Learning easier to use?
Awesome Machine Learning is generally the easier of the two to get started with, while Applied ML rewards more setup with more control.
Are Applied ML and Awesome Machine Learning free?
Applied 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 Applied ML and Awesome Machine Learning locally?
Applied ML: yes · Awesome Machine Learning: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Applied ML vs Awesome Machine Learning — which should I pick in 2026?
Choose Applied ML for learning from what companies really did. Choose Awesome Machine Learning for finding the right library in any language.
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