ML Systems Textbook by Havard
7 days ago
- #Systems Design
- #Machine Learning
- #AI Engineering
- Machine Learning Systems provides a systematic framework for understanding and engineering ML systems.
- The book bridges the gap between theoretical foundations and practical engineering, focusing on the systems perspective.
- Emphasizes broader context including data engineering, model optimization, hardware-aware training, and inference acceleration.
- Aims to develop the ability to reason about ML system architectures and apply enduring engineering principles.
- The book emerged from collaborative work in CS249r at Harvard University, involving students, faculty, and industry partners.
- The project has a global outreach goal, aiming for 10,000 GitHub stars with support from sponsors like the EDGE AI Foundation.
- Encourages contributions from the community, including feedback, corrections, and new ideas via GitHub.