At scale, machine learning changes its engineering requirements: models grow too large for a single accelerator, services span continents, and deployments carry responsibilities to society. This book addresses AI engineering at scale. The treatment follows the lifecycle of a large-scale system: defining the distributed architecture, building the physical infrastructure fleet, ensuring operational reliability, deploying to global users, and hardening the system for safety and responsibility.