Scale AI: Architecting High-Performance Machine Learning on Google Kubernetes Engine and Terraform Training a model in a Jupyter Notebook is one thing. Running it in production at scale is another world entirely. This book is the bridge between those two worlds—a comprehensive guide for ML engineers, platform engineers, and data scientists who need to build production-grade AI infrastructure that can handle billions of parameters, thousands of requests per second, and cloud bills that don't spiral out of control. From securing GPU quotas to implementing distributed training with PyTorch FSDP, from serving models with vLLM to autoscaling on custom metrics, this book takes you from a blank Google Cloud project to a sophisticated AI hypercomputer. You'll master Kubernetes not as a container orchestrator, but as the operating system for modern AI. You'll learn Terraform not as an automation tool, but as the blueprint for reproducible, auditable infrastructure. With practical code examples, real-world architectural patterns, and hard-won lessons on cost optimization, this is the definitive guide to building AI systems that scale. About The Author GK Marballi has spent 20+ years turning data into competitive advantage for global brands---from Priceline to S&P Global and Barnes & Noble. He has led high-impact product and analytics teams, and navigated the front lines of the AI revolution. He is based in New York City and holds an MBA from Harvard Business School.