Kubernetes for Generative AI Solutions: A complete guide to designing, optimizing, and deploying Generative AI workloads on

Master the complete Generative AI project lifecycle on Kubernetes (K8s) from design and optimization to deployment using best practices, cost-effective strategies, and real-world examples.

Key Features
Build and deploy your first Generative AI workload on Kubernetes with confidence
Learn to optimize costly resources such as GPUs using fractional allocation, Spot Instances, and automation
Gain hands-on insights into observability, infrastructure automation, and scaling Generative AI workloads

Book Description
Generative AI (GenAI) is revolutionizing industries, from chatbots to recommendation engines to content creation, but deploying these systems at scale poses significant challenges in infrastructure, scalability, security, and cost management.

This book is your practical guide to designing, optimizing, and deploying GenAI workloads with Kubernetes (K8s) the leading container orchestration platform trusted by AI pioneers. Whether you're working with large language models, transformer systems, or other GenAI applications, this book helps you confidently take projects from concept to production. You’ll get to grips with foundational concepts in machine learning and GenAI, understanding how to align projects with business goals and KPIs. From there, you'll set up Kubernetes clusters in the cloud, deploy your first workload, and build a solid infrastructure. But your learning doesn't stop at deployment. The chapters highlight essential strategies for scaling GenAI workloads in production, covering model optimization, workflow automation, scaling, GPU efficiency, observability, security, and resilience.

By the end of this book, you’ll be fully equipped to confidently design and deploy scalable, secure, resilient, and cost-effective GenAI solutions on Kubernetes.

What you will learn
Explore GenAI deployment stack, agents, RAG, and model fine-tuning
Implement HPA, VPA, and Karpenter for efficient autoscaling
Optimize GPU usage with fractional allocation, MIG, and MPS setups
Reduce cloud costs and monitor spending with Kubecost tools
Secure GenAI workloads with RBAC, encryption, and service meshes
Monitor system health and performance using Prometheus and Grafana
Ensure high availability and disaster recovery for GenAI systems
Automate GenAI pipelines for continuous integration and delivery
Who this book is for
This book is for solutions architects, product managers, engineering leads, DevOps teams, GenAI developers, and AI engineers. It's also suitable for students and academics learning about GenAI, Kubernetes, and cloud-native technologies. A basic understanding of cloud computing and AI concepts is needed, but no prior knowledge of Kubernetes is required.

Table of Contents
GenAI—Intro, Evolution, and Project Lifecycle
K8s—Introduction and Integration with GenAI
Getting Started with K8s in the Cloud
GenAI Model Optimization for Domain-Specific Use Cases (RAG, Fine Tuning, etc.)
Getting Started with GenAI on K8s—Chatbot Example
Deploying GenAI on K8s—Scaling Best Practices
Deploying GenAI on K8s—Cost Optimization Best Practices
Deploying GenAI on K8s—Networking Best Practices
Deploying GenAI on K8s—Security Best Practices
Optimizing GPU Resources in K8s for GenAI Applications
GenAIOps: Creating GenAI Automation Pipeline
Getting Visibility into GenAI Workloads Resource Utilization
High Availability and Disaster Recovery Implementation
Wrap Up and Further Readings