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AI Infrastructure Engineering: Building GPU Systems, Optimizing Inference, Designing Distributed Architectures, and Running Production Deployments
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
Architect and deploy high-performance GPU clustersOptimize inference pipelines for speed, cost, and efficiencyDesign and manage distributed training and serving architecturesImplement production-grade monitoring, scaling, and reliability practicesNavigate 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.
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
Architect and deploy high-performance GPU clustersOptimize inference pipelines for speed, cost, and efficiencyDesign and manage distributed training and serving architecturesImplement production-grade monitoring, scaling, and reliability practicesNavigate 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.
482 pages, Kindle Edition
Published May 20, 2026
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