Kubernetes for AI Engineers

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

• 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

• AI Engineers
• MLOps Engineers
• DevOps Professionals
• Platform Engineers
• Machine Learning Engineers
• Cloud Architects
• Developers building LLM applications
• AI startups and technical founders

• 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.

• 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

• LLM platforms,
• AI APIs,
• RAG systems,
• inference clusters,
• production AI services,