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Kubernetes AI is a hands-on guide to building and operating production AI platforms on Kubernetes. Written for platform engineers, DevOps teams, SREs, and ML practitioners, it takes you from GPU enablement to reliable inference, training, and multi-tenant operations.
You'll learn how to install and manage the NVIDIA GPU stack, schedule and share accelerators, serve large language models with vLLM and Kubernetes-native tools, and optimize inference performance and cost. The book also covers distributed training, batch workloads, AI storage and networking, GPU benchmarking, observability, security, and resource governance.
Whether you are building an internal AI platform or moving machine-learning experiments into production, this book provides practical architectures, deployment patterns, and operational guidance you can apply to real Kubernetes clusters.
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