Overview
Machine Learning Systems (MLSysBook) is an open-source textbook derived from Harvard’s CS249r course. It teaches engineers how to build scalable, maintainable, and auditable AI systems covering data engineering, system design, model deployment, MLOps, and edge AI. The project provides online reading, downloadable PDF/EPUB, labs, and course materials for teaching and self-study.
Key features
- Comprehensive ML systems coverage from data pipelines to production monitoring.
- Rich teaching materials: HTML, PDF/EPUB, labs and instructor resources for classroom use.
- Community-driven open-source development with continuous integration and multi-format publishing.
Use cases
- University courses and classroom instruction for ML systems engineering.
- Corporate training to help engineering teams operationalize ML models reliably.
- Self-learners and researchers studying practical ML system design and deployment.
Technical highlights
- Built with Quarto and Book Binder tooling, supporting multiple output formats and reproducible labs.
- Includes automation scripts and binder configurations to reproduce lab environments locally or in the cloud.
- Uses GitHub Actions and preview deployments to maintain up-to-date course content and CI-based quality checks.