Isbn: 9781098171926 - generative ai on kubernetes: operationalizing large language models (10 Ergebnisse)

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Brand New. 250 pages. 9.19x7.00x9.19 inches. In Stock.

Paperback. Zustand: Brand New. 250 pages. 9.19x7.00x9.19 inches. In Stock.

Zustand: New. 2026. paperback. . . . . . Books ship from the US and Ireland.

Zustand: New.

Taschenbuch. Zustand: Neu. Generative AI on Kubernetes | Operationalizing Large Language Models | Roland Huss (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2026 | O'Reilly Media | EAN 9781098171926 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

Taschenbuch. Zustand: Neu. Neuware -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 stateLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 250 pp. Englisch.…

Taschenbuch. Zustand: Neu. Neuware - 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.…

Taschenbuch. Zustand: Neu. Neuware -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 250 pp. Englisch.…