applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
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.
From the README
- How the problem is framed 🔎(e.g., personalization as recsys vs. search vs. sequences)
- What machine learning techniques worked ✅ (and sometimes, what didn't ❌)
- Why it works, the science behind it with research, literature, and references 📂
- What real-world results were achieved (so you can better assess ROI ⏰💰📈)
Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.
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applied-ml has 29.9k stars on GitHub. It has been forked 4k times. It has been in active development since 2020. applied-ml is available under the MIT license. Its main topics are applied-data-science, applied-machine-learning, computer-vision, data-discovery.
Read the full guideWhat is applied-ml?
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
Is applied-ml open source?
applied-ml is an open-source project. It is released under the MIT license.
Is applied-ml free?
Yes. applied-ml is free and open source — you can use, modify and self-host it.
What license does applied-ml use?
applied-ml is available under the MIT license.
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