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 FeaturesBuild and deploy your first Generative AI workload on Kubernetes with confidenceLearn to optimize costly resources such as GPUs using fractional allocation, Spot Instances, and automationGain hands-on insights into observability, infrastructure automation, and scaling Generative AI workloadsPurchase of the print or Kindle book includes a free PDF eBookBook 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 learnExplore GenAI deployment stack, agents, RAG, and model fine-tuningImplement HPA, VPA, and Karpenter for efficient autoscalingOptimize GPU usage with fractional allocation, MIG, and MPS setupsReduce cloud costs and monitor spending with Kubecost toolsSecure GenAI workloads with RBAC, encryption, and service meshesMonitor system health and performance using Prometheus and GrafanaEnsure high availability and disaster recovery for GenAI systemsAutomate GenAI pipelines for continuous integration and deliveryWho 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 ContentsGenAI—Intro, Evolution, and Project LifecycleK8s—Introduction and Integration with GenAIGetting Started with K8s in the CloudGenAI Model Optimization for Domain-Specific Use Cases (RAG, Fine Tuning, etc.)Getting Started with GenAI on K8s—Chatbot ExampleDeploying GenAI on K8s—Scaling Best PracticesDeploying GenAI on K8s—Cost Optimization Best PracticesDeploying GenAI on K8s—Networking Best PracticesDeploying GenAI on K8s—Security Best PracticesOptimizing GPU Resources in K8s for GenAI ApplicationsGenAIOps: Creating GenAI Automation PipelineGetting Visibility into GenAI Workloads Resource UtilizationHigh Availability and Disaster Recovery ImplementationWrap Up and Further Readings
From the Publisher
GenAIOps on K8s
This book presents a comprehensive lifecycle overview of Generative AI (GenAI) applications, covering every stage from data preparation to deployment and monitoring within Kubernetes environments.
It introduces methods for organizing and cleaning data to support experimentation, followed by a structured framework for evaluating and selecting suitable foundation models (FMs) or large language models (LLMs) based on specific use case requirements. Key adaptation techniques covered include:
Fine-tuning for task-specific performanceKnowledge distillation for creating lighter, more efficient modelsPrompt engineering for rapid customization
Once adapted, models are deployed using scalable serving strategies, with monitoring systems in place to ensure consistent performance, detect data drift, and support continuous refinement.
Kubernetes (K8s) plays a central role throughout this lifecycle by providing the scalability, flexibility, and automation necessary for training, orchestration, and experiment tracking. Within the Kubernetes ecosystem, the following tools are highlighted:
Kubeflow for end-to-end machine learning workflowsMLflow for experiment tracking and model lifecycle managementArgo Workflows for defining and managing complex pipelinesRay for distributed computing and scalable model operations
This holistic approach to GenAIOps emphasizes performance optimization alongside ethical considerations, resulting in a scalable, repeatable, and trustworthy framework for managing GenAI applications.
Scaling GenAI Applications on Kubernetes
Application scaling in Kubernetes enables dynamic adjustment of resources based on workload demand, promoting efficient utilization, cost savings, and reliable performance. Kubernetes offers a range of scaling mechanisms that respond to metrics such as CPU usage, memory consumption, and custom-defined indicators.
In this book, we will cover the core components of Kubernetes scaling, including scaling metrics, the Horizontal Pod Autoscaler (HPA), the Vertical Pod Autoscaler (VPA), Kubernetes Event-Driven Autoscaling (KEDA), the Cluster Autoscaler, and Karpenter.
Cost Optimization of GenAI Applications on Kubernetes
We will examine the main cost factors in deploying Generative AI applications in the cloud, including compute, storage, and networking. You will also explore practical ways to reduce costs, such as right-sizing resources, optimizing storage, and following networking best practices. You’ll also learn how to use tools like Kubecost to monitor usage and identify savings.
Networking for GenAI Applications on Kubernetes
This book explores essential cloud networking best practices for deploying Generative AI applications on Kubernetes.
Key topics include:
Understanding the Kubernetes networking modelManaging advanced traffic flows using service meshesSecuring GenAI workloads with Kubernetes network policiesOptimizing network performance for GenAI environments
Securing GenAI Applications on Kubernetes
This book outlines security best practices for deploying GenAI applications on Kubernetes.
Topics covered include:
Defense in DepthKubernetes Security ConsiderationsGenAI-Specific Security Challenges and RecommendationsApplying Security Best Practices in a Chatbot Application
ASIN : B0F98KKSB1
Publisher : Packt Publishing
Accessibility : Learn more
Publication date : June 6, 2025
Edition : 1st
Language : English
File size : 17.3 MB
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 558 pages
ISBN-13 : 978-1836209928
Page Flip : Enabled
Best Sellers Rank: #1,532,290 in Kindle Store (See Top 100 in Kindle Store) #149 in Distributed Systems & Computing #1,362 in Computer Networks, Protocols & APIs (Books) #2,546 in AI & Semantics
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