Kubernetes for AI Infrastructure: The Engineer's Guide to GPU Orchestration and Reducing MLOps Overhead in Production
Your 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.
Kubernetes for AI Infrastructure 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.
Inside, readers will learn how to:
Build GPU-ready Kubernetes clusters for AI workloadsOrchestrate NVIDIA H100, B200, MIG, and DRA-based resourcesRun distributed PyTorch training with Kueue, Volcano, and KubeflowServe LLMs at scale using vLLM, KServe, Gateway API, and canary deploymentsReduce GPU waste with Karpenter, autoscaling, quotas, and FinOps strategiesSecure AI pods with workload identity, zero-trust networking, and policy enforcementMonitor GPU utilization, inference latency, scheduling bottlenecks, and cluster healthThis 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.
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