Generative AI on Kubernetes

Operationalizing Large Language Models
This book serves as a practical, hands-on guide for MLOps engineers, software developers, Kubernetes administrators, and AI professionals ready to unlock AI innovation with the power of cloud native infrastructure.
Generative AI is revolutionizing industries, and Kubernetes has fast become the backbone for deploying and managing these resource-intensive workloads. This book serves as a practical, hands-on guide for MLOps engineers, software developers, Kubernetes administrators, and AI professionals ready to unlock AI innovation with the power of cloud native infrastructure. Authors Roland Huss and Daniele Zonca provide a clear road map for training, fine-tuning, deploying, and scaling GenAI models on Kubernetes, addressing challenges like resource optimization, automation, and security along the way.

With actionable insights with real-world examples, readers will learn to tackle the opportunities and complexities of managing GenAI applications in production environments. Whether you're experimenting with large-scale language models or facing the nuances of AI deployment at scale, you'll uncover expertise you need to operationalize this exciting technology effectively.

• Learn to run GenAI models on Kubernetes for efficient scalability
• Get techniques to train and fine-tune LLMs within Kubernetes environments
• See how to deploy production-ready AI systems with automation and resource optimization
• Discover how to monitor and scale GenAI applications to handle real-world demand
• Uncover the best tools to operationalize your GenAI workloads
• Learn how to run agent-based and AI-driven applications

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