Overview

Kubernetes for AI Engineers Deploy, Scale, and Orchestrate LLM Workloads in Production Artificial Intelligence is evolving fast-and running models locally is no longer enough. Modern AI systems must be scalable, GPU-optimized, cloud-native, secure, and production-ready. That's where Kubernetes becomes essential. Kubernetes for AI Engineers is a practical, production-focused guide for AI engineers, MLOps professionals, DevOps teams, platform engineers, and developers building modern LLM infrastructure. Unlike generic Kubernetes books focused on traditional applications, this book is built specifically for AI workloads. You'll learn how to deploy, manage, optimize, and scale large language models (LLMs), GPU inference systems, vector databases, and AI pipelines using Kubernetes in real-world environments. From Docker containers to enterprise-grade orchestration, this book bridges the gap between experimentation and production AI deployment. Inside This Book, You'll Learn How To: Understand Kubernetes fundamentals for AI workloads Deploy and orchestrate containerized LLM applications Configure GPU node pools for high-performance inference Scale AI infrastructure with Kubernetes clusters Use Helm for model serving and deployment Implement HPA and KEDA autoscaling for inference workloads Deploy vector databases and RAG systems Build Kubeflow pipelines for AI workflow automation Secure AI clusters using RBAC, Secrets, and policies Monitor AI systems with Prometheus and Grafana Optimize GPU scheduling, memory usage, and performance Design multi-cluster and hybrid AI architectures Troubleshoot production AI deployments and networking issues Real-World Technologies Covered Kubernetes for AI workloads GPU scheduling and CUDA containers LLM inference orchestration KServe and model serving Kubeflow pipelines Docker + Kubernetes workflows Vector databases and RAG systems Distributed AI infrastructure AI observability and monitoring CI/CD for AI systems Multi-node GPU deployments Cloud-native AI infrastructure Who This Book Is For Perfect for: AI Engineers MLOps Engineers DevOps Professionals Platform Engineers Machine Learning Engineers Cloud Architects Developers building LLM applications AI startups and technical founders Deploying your first AI inference service or building enterprise-scale AI platforms, this book provides the practical skills needed with Kubernetes. Why This Book Is Different Most Kubernetes books teach generic container orchestration. This book teaches: Kubernetes specifically for AI systems. You'll learn: how GPUs behave inside Kubernetes, how LLM inference scales, how AI workloads differ from traditional applications, and how to build resilient AI infrastructure for production environments. Every chapter focuses on practical deployment, scalability, observability, performance optimization, and modern AI DevOps workflows. Includes Practical Resources & Templates Inside, you'll also get: Kubernetes manifests for AI workloads Helm examples GPU optimization strategies Security and secret-management workflows AI observability templates Deployment architecture patterns Troubleshooting and debugging guides Build the Future of AI Infrastructure Kubernetes is becoming the foundation of scalable AI systems across startups, enterprises, and cloud platforms worldwide. If you want to build: LLM platforms, AI APIs, RAG systems, inference clusters, production AI services,

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