AI Infrastructure Engineering: Building GPU Systems, Optimizing Inference, Designing Distributed Architectures, and Running Production Deployments

Bol

Reactive PublishingMaster the full stack of modern AI infrastructure, from raw hardware to large-scale production systems.This practical guide delivers the essential knowledge and techniques used by today's AI infrastructure engineers and platform teams. You will learn how to design, build, optimize, and operate reliable GPU-powered systems that power real-world AI workloads at scale.Inside the book: - Architect and deploy high-performance GPU clusters- Optimize inference pipelines for speed, cost, and efficiency- Design and manage distributed training and serving architectures- Implement production-grade monitoring, scaling, and reliability practices- Navigate the trade-offs between on-prem, cloud, and hybrid environmentsWritten for engineers, architects, and technical leaders, this book bridges the gap between theoretical machine learning and the complex realities of running AI in production. Whether you are building your first GPU cluster or scaling an existing platform to thousands of accelerators, you will find actionable strategies and battle-tested patterns you can apply immediately.Clear, up-to-date, and focused on real engineering challenges, not hype.

Amazon

Pages: 447, Paperback, Independently published