ML Systems Textbook by Havard (www.mlsysbook.ai)

Harvard’s "Machine Learning Systems" textbook—an early-access preview slated for MIT Press (2026)—offers a systems-first framework aimed at closing the gap between model-centric ML education and the engineering needed to run models in production. Born from the CS249r course (Fall 2023) and built with student, faculty, and industry contributions, the book emphasizes practical system design: data engineering and pipelines, model optimization, hardware-aware training, and inference acceleration. Rather than focusing only on algorithms, it teaches how to reason about end-to-end ML architectures and apply durable engineering principles for flexible, efficient, and robust deployments. For the AI/ML community this matters because the next bottleneck won’t just be better models but the engineers and infrastructure that scale, sustain, and speed them. Technical takeaways include approaches to hardware-aware training, latency- and throughput-oriented inference strategies, pipeline and data-management patterns, and trade-offs for energy and cost efficiency. The project is open and collaborative—hosted on GitHub with a 2025 goal of 10,000 stars (matched by the EDGE AI Foundation for funding)—and is complemented by a short podcast created with Google’s Notebook LM and an IEEE education viewpoint. Practitioners and educators can use the text as a hands-on curriculum to train engineers who make ML systems reliable in the real world.