Kubernetes for Generative AI Solutions: A complete guide to designing, optimizing, and deploying Generative AI workloads on Kubernetes
Author: Ashok Srirama (Author), Sukirti Gupta (Author)
Publisher finelybook 出版社: Packt Publishing
Publication Date 出版日期: 2025-06-06
Language 语言: English
Print Length 页数: 334 pages
ISBN-10: 1836209932
ISBN-13: 9781836209935
Book Description
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
- Purchase of the print or Kindle book includes a free PDF eBook
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
Review
“A concise field guide that bridges GenAI theory with the gritty reality of running LLMs on Kubernetes, covering practical patterns, security tips, and cost-savvy recipes for taking prototypes to production.
Vikash Rungta, Lead Product Manager (ex-Llama Safety, Meta); Instructor, Generative AI for PMs at Stanford Continuing Studies
“A practical guide that connects GenAI research with real-world Kubernetes operations. From model architecture to GPU scheduling, this book covers the full development and deployment lifecycle with clarity and depth.”
Gaurav Agarwal, Founder and CEO, RagaAI
“Kubernetes for Generative AI Solutions delivers exceptional value by addressing the critical intersection of technology and business outcomes. For leaders navigating digital transformation, this book showcases the role of Kubernetes as the architectural foundation to deploy enterprise-grade GenAI applications. The recommendations for cost optimization strategies, security frameworks, and high-availability architectures speak directly to boardroom concerns of balancing innovation with operational efficiency. Through practical examples, including e-commerce use cases, it shows how organizations can leverage this technology to drive revenue growth through personalized AI experiences.”
Stephen Di Mauro, Head of U.S. Solution Architecture, AWS
About the Author
Ashok Srirama is a Principal Specialist Solutions Architect at Amazon Web Services (AWS) with over 19 years of IT experience, specializing in cloud architecture, distributed systems, Kubernetes, and Generative AI. Recognized with the prestigious AWS Gold Jacket and Kubestronaut accreditation, he has authored numerous technical publications, presented at 25+ tech summits, and created AWS solutions for enterprise container deployments.
Sukirti Gupta has over 15 years of experience spanning Cloud Computing, Kubernetes, Generative AI, and Data Center Architecture. Sukirti currently leads go to market strategy for AWS (Amazon Web Services), supporting customers with their GenAI journey and has played pivotal roles at AWS, AMD, and Intel Corporation.
