{"product_id":"kubernetes-for-ai-infrastructure-ethan-tyson-9798258703552","title":"Kubernetes for AI Infrastructure: The Engineer's Guide to GPU Orchestration and Reducing MLOps Overhead in Production","description":"\u003cb\u003eKubernetes for AI Infrastructure: The Engineer's Guide to GPU Orchestration and Reducing MLOps Overhead in Production\u003c\/b\u003e\u003cp\u003eYour AI workloads are scaling faster than your infrastructure can handle. GPU clusters are expensive, distributed training is fragile, inference latency is unforgiving, and MLOps teams are under pressure to ship reliable systems without wasting compute.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eKubernetes for AI Infrastructure\u003c\/b\u003e gives engineers a production-focused guide to building, scaling, securing, and optimizing Kubernetes environments for modern AI workloads. Written for platform engineers, MLOps practitioners, DevOps teams, and systems architects, this book shows how to turn Kubernetes into a high-performance AI control plane for GPU orchestration, distributed training, LLM inference, observability, security, and cost management.\u003c\/p\u003e\u003cp\u003eInside, readers will learn how to: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eBuild GPU-ready Kubernetes clusters for AI workloads\u003c\/li\u003e\n\u003cli\u003eOrchestrate NVIDIA H100, B200, MIG, and DRA-based resources\u003c\/li\u003e\n\u003cli\u003eRun distributed PyTorch training with Kueue, Volcano, and Kubeflow\u003c\/li\u003e\n\u003cli\u003eServe LLMs at scale using vLLM, KServe, Gateway API, and canary deployments\u003c\/li\u003e\n\u003cli\u003eReduce GPU waste with Karpenter, autoscaling, quotas, and FinOps strategies\u003c\/li\u003e\n\u003cli\u003eSecure AI pods with workload identity, zero-trust networking, and policy enforcement\u003c\/li\u003e\n\u003cli\u003eMonitor GPU utilization, inference latency, scheduling bottlenecks, and cluster health\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThis is not a beginner's Kubernetes book. It is a practical engineering guide for teams running real AI systems in production, where every idle GPU, failed job, and poor scheduling decision costs money. The uploaded manuscript positions the book around production AI infrastructure, GPU-aware scheduling, MLOps overhead reduction, and secure hyperscale deployment patterns.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Ethan Tyson\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798258703552\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 04\/24\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 150\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 0.60lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 10.00h x 7.00w x 0.32d","brand":"Ethan Tyson","offers":[{"title":"Paperback","offer_id":49172972830975,"sku":"9798258703552","price":20.0,"currency_code":"USD","in_stock":true}],"url":"https:\/\/www.whiterainbookhouse.com\/products\/kubernetes-for-ai-infrastructure-ethan-tyson-9798258703552","provider":"WR Book House","version":"1.0","type":"link"}