Made With ML vs 
Applied ML
Made With ML vs Applied ML compared for 2026 — features, license, ease of use, performance and which one to choose. From notebook to production system vs How real companies actually ship ML.
Updated regularly · curated by OpenSourceAI.tech
Made With ML vs Applied ML at a glance
| Spec | Made With ML | Applied ML |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Course (MLOps) | Curated papers & posts |
| License | MIT | MIT |
| Runs locally | Yes | Yes |
| Primary language | Python | Markdown |
| Ease of use | Intermediate | Intermediate |
| Best for | the gap between a notebook and production | learning from what companies really did |
| GitHub stars | 48.7k | 29.9k |
How Made With ML and Applied ML score
| Criterion | Made With ML | Applied ML |
|---|---|---|
| Popularity | 4.0 | 3.5 |
| Maintenance | 4.0 | 2.0 |
| 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
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
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
Key differences
Made With ML is course (MLOps), while Applied ML is curated papers & posts. In short, Made With ML fits the gap between a notebook and production, and Applied ML fits learning from what companies really did.
Which should you choose?
Choose Made With ML for the gap between a notebook and production. Choose Applied ML for learning from what companies really did.
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 Applied ML easier to use?
Both sit at a similar level (Intermediate). Your choice should come down to fit rather than difficulty.
Are Made With ML and Applied ML free?
Made With ML is free and open source (MIT), and Applied ML is free and open source (MIT). Neither charges for the core software.
Can I run Made With ML and Applied ML locally?
Made With ML: yes · Applied ML: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Made With ML vs Applied ML — which should I pick in 2026?
Choose Made With ML for the gap between a notebook and production. Choose Applied ML for learning from what companies really did.
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