Kubernetes for AI Engineers: Deploy, Scale, and Orchestrate LLM Workloads in Production (The AI Engineers Series Book 2)

Kubernetes for AI EngineersDeploy, Scale, and Orchestrate LLM Workloads in ProductionArtificial 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 workloadsDeploy and orchestrate containerized LLM applicationsConfigure GPU node pools for high-performance inferenceScale AI infrastructure with Kubernetes clustersUse Helm for model serving and deploymentImplement HPA and KEDA autoscaling for inference workloadsDeploy vector databases and RAG systemsBuild Kubeflow pipelines for AI workflow automationSecure AI clusters using RBAC, Secrets, and policiesMonitor AI systems with Prometheus and GrafanaOptimize GPU scheduling, memory usage, and performanceDesign multi-cluster and hybrid AI architecturesTroubleshoot production AI deployments and networking issuesReal-World Technologies CoveredKubernetes for AI workloadsGPU scheduling and CUDA containersLLM inference orchestrationKServe and model servingKubeflow pipelinesDocker + Kubernetes workflowsVector databases and RAG systemsDistributed AI infrastructureAI observability and monitoringCI/CD for AI systemsMulti-node GPU deploymentsCloud-native AI infrastructureWho This Book Is ForPerfect for:AI EngineersMLOps EngineersDevOps ProfessionalsPlatform EngineersMachine Learning EngineersCloud ArchitectsDevelopers building LLM applicationsAI startups and technical foundersDeploying your first AI inference service or building enterprise-scale AI platforms, this book provides the practical skills needed with Kubernetes.Why This Book Is DifferentMost 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 & TemplatesInside, you’ll also get:Kubernetes manifests for AI workloadsHelm examplesGPU optimization strategiesSecurity and secret-management workflowsAI observability templatesDeployment architecture patternsTroubleshooting and debugging guidesBuild the Future of AI InfrastructureKubernetes 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, Read more