Annotated Paper Implementations vs 
Made With ML
Annotated Paper Implementations vs Made With ML compared for 2026 — features, license, ease of use, performance and which one to choose. 60+ papers implemented and explained side by side vs From notebook to production system.
Updated regularly · curated by OpenSourceAI.tech
Annotated Paper Implementations vs Made With ML at a glance
| Spec | Annotated Paper Implementations | Made With ML |
|---|---|---|
| Category | Learn AI & machine learning | Learn AI & machine learning |
| Type | Reference implementations | Course (MLOps) |
| License | MIT | MIT |
| Runs locally | Yes | Yes |
| Primary language | Python | Python |
| Ease of use | Advanced | Intermediate |
| Best for | reading a paper and seeing exactly how it is built | the gap between a notebook and production |
| GitHub stars | 67.1k | 48.7k |
How Annotated Paper Implementations and Made With ML score
| Criterion | Annotated Paper Implementations | Made With ML |
|---|---|---|
| Popularity | 4.5 | 4.0 |
| Maintenance | 4.0 | 4.0 |
| Ease of use | 2.5 | 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
Annotated Paper Implementations
labml.ai's collection of deep learning papers implemented in PyTorch, with the explanation printed alongside the code — transformers, diffusion, RL, optimisers and more.
- Paper and code side by side, always in sync
- 60+ architectures, all runnable
- The fastest way to understand a new paper
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
Annotated Paper Implementations is reference implementations, while Made With ML is course (MLOps). Annotated Paper Implementations leans more advanced-friendly, whereas Made With ML is more suited to intermediate users. In short, Annotated Paper Implementations fits reading a paper and seeing exactly how it is built, and Made With ML fits the gap between a notebook and production.
Which should you choose?
Choose Annotated Paper Implementations for reading a paper and seeing exactly how it is built. 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 Annotated Paper Implementations or Made With ML easier to use?
Made With ML is generally the easier of the two to get started with, while Annotated Paper Implementations rewards more setup with more control.
Are Annotated Paper Implementations and Made With ML free?
Annotated Paper Implementations is free and open source (MIT), and Made With ML is free and open source (MIT). Neither charges for the core software.
Can I run Annotated Paper Implementations and Made With ML locally?
Annotated Paper Implementations: yes · Made With ML: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
Annotated Paper Implementations vs Made With ML — which should I pick in 2026?
Choose Annotated Paper Implementations for reading a paper and seeing exactly how it is built. Choose Made With ML for the gap between a notebook and production.
People also compare
Explore more open-source AI
Browse thousands of open-source AI tools, models and projects — all curated in one place, updated daily.
Explore the directory →