Design Principles

This part focuses on the architectural and design principles that underpin the development of machine learning systems. It examines the full ML workflow, from data engineering and training methodologies to the design and implementation of software frameworks that coordinate computational graphs and interface with underlying hardware. The chapters also address optimization strategies for computational efficiency, hardware-aware system design, and the role of benchmarking in evaluating system performance. Together, these topics form the basis for building ML systems that are modular, efficient, and scalable across a range of deployment scenarios.