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Generative AI on Kubernetes
Generative AI on Kubernetes
Knygos.lt klubas Knygos.lt nariams
63,48 €
-30%
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90,69 €
  • Išsiųsime per 10–14 d.d.
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 Kub…
  • Leidėjas:
  • Metai: 2026
  • Puslapiai: 404
  • ISBN-10: 1098171926
  • ISBN-13: 9781098171926
  • Formatas: 17.8 x 23.3 x 2.1 cm, minkšti viršeliai
  • Kalba: Anglų

Generative AI on Kubernetes (el. knyga) (skaityta knyga) | knygos.lt

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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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  • Autorius: Roland Huss, Daniele Zonca
  • Leidėjas:
  • Metai: 2026
  • Puslapiai: 404
  • ISBN-10: 1098171926
  • ISBN-13: 9781098171926
  • Formatas: 17.8 x 23.3 x 2.1 cm, minkšti viršeliai
  • Kalba: Anglų

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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