Build your own ML framework. TinyTorch is organized into four progressive tiers that take you from mathematical foundations to production-ready systems. Each tier builds on the previous one, teaching you not just how to code ML components, but how they work together as a complete system.

Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML) systems. This textbook bridges the gap between theoretical foundations and practical engineering, emphasizing the systems perspective required to build effective AI solutions. Unlike resources that focus primarily on algorithms and model architectures, this book highlights the broader context in which ML systems operate, including data engineering, model optimization, hardware-aware training, and inference acceleration. Readers will develop the ability to reason about ML system architectures and apply enduring engineering principles for building flexible, efficient, and robust machine learning systems.

The Rules of Machine Learning guide provides a set of best practices and distilled wisdom from Google engineers for building, deploying, and maintaining robust and effective Machine Learning systems in production.