Isbn: 9798197947581 - ai infrastructure engineering: building gpu systems, optimizing inference, designing distributed architectures, and running production deployments (6 resultados)

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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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EUR 39,85
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 54,76
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Taschenbuch. Condición: Neu. Neuware - Reactive PublishingMaster the full stack of modern AI infrastructure, from raw hardware to large-scale production systems.This practical guide delivers the essential knowledge and techniques used by today's AI infrastructure engineers and platform teams. You will learn how to design, build, optimize, and operate reliable GPU-powered systems that power real-world AI workloads at scale.Inside the book: - Architect and deploy high-performance GPU clusters- Optimize inference pipelines for speed, cost, and efficiency- Design and manage distributed training and serving architectures- Implement production-grade monitoring, scaling, and reliability practices- Navigate the trade-offs between on-prem, cloud, and hybrid environmentsWritten for engineers, architects, and technical leaders, this book bridges the gap between theoretical machine learning and the complex realities of running AI in production. Whether you are building your first GPU cluster or scaling an existing platform to thousands of accelerators, you will find actionable strategies and battle-tested patterns you can apply immediately.Clear, up-to-date, and focused on real engineering challenges, not hype.…

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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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EUR 37,53
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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EUR 36,00
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Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
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EUR 40,00
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponible
Paperback. Condición: new. Paperback. Reactive PublishingMaster the full stack of modern AI infrastructure, from raw hardware to large-scale production systems.This practical guide delivers the essential knowledge and techniques used by today's AI infrastructure engineers and platform teams. You will learn how to design, build, optimize, and operate reliable GPU-powered systems that power real-world AI workloads at scale.Inside the book: Architect and deploy high-performance GPU clustersOptimize inference pipelines for speed, cost, and efficiencyDesign and manage distributed training and serving architecturesImplement production-grade monitoring, scaling, and reliability practicesNavigate the trade-offs between on-prem, cloud, and hybrid environmentsWritten for engineers, architects, and technical leaders, this book bridges the gap between theoretical machine learning and the complex realities of running AI in production. Whether you are building your first GPU cluster or scaling an existing platform to thousands of accelerators, you will find actionable strategies and battle-tested patterns you can apply immediately.Clear, up-to-date, and focused on real engineering challenges, not hype. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
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EUR 39,61
Envío por EUR 43,80Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponible
Paperback. Condición: new. Paperback. Reactive PublishingMaster the full stack of modern AI infrastructure, from raw hardware to large-scale production systems.This practical guide delivers the essential knowledge and techniques used by today's AI infrastructure engineers and platform teams. You will learn how to design, build, optimize, and operate reliable GPU-powered systems that power real-world AI workloads at scale.Inside the book: Architect and deploy high-performance GPU clustersOptimize inference pipelines for speed, cost, and efficiencyDesign and manage distributed training and serving architecturesImplement production-grade monitoring, scaling, and reliability practicesNavigate the trade-offs between on-prem, cloud, and hybrid environmentsWritten for engineers, architects, and technical leaders, this book bridges the gap between theoretical machine learning and the complex realities of running AI in production. Whether you are building your first GPU cluster or scaling an existing platform to thousands of accelerators, you will find actionable strategies and battle-tested patterns you can apply immediately.Clear, up-to-date, and focused on real engineering challenges, not hype. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…