Generative AI on Kubernetes
Operationalizing Large Language Models
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 combine 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 how to deploy LLMs more efficiently with optimized inference runtimes - Get hands-on with GPU scheduling, including hardware detection and multinode scaling - Monitor and understand LLM-specific metrics like Time to First Token and token throughput - Know when to fine-tune a model or when retrieval augmentation is the better choice - Discover how to evaluate models with standardized benchmarks before committing GPU resources - Learn to run agentic applications with secure tool integration, identity management, and persistent state
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