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

Ashok Srirama, Sukirti Gupta

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

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.