Kubernetes for Generative AI Solutions: A complete guide to designing optimizing and deploying Generative AI workloads (Paperback)

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 a