Kubernetes for Generative AI Solutions

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 DescriptionGenerative 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 forThis 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.]]>