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Destinations, frais et délaisVendeur : California Books, Miami, FL, Etats-Unis
Etat : New. N° de réf. du vendeur I-9781803237015
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Vendeur : PBShop.store US, Wood Dale, IL, Etats-Unis
PAP. Etat : New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9781803237015
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Vendeur : PBShop.store UK, Fairford, GLOS, Royaume-Uni
PAP. Etat : New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. N° de réf. du vendeur L0-9781803237015
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Vendeur : THE SAINT BOOKSTORE, Southport, Royaume-Uni
Paperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 100. N° de réf. du vendeur C9781803237015
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Etat : New. In. N° de réf. du vendeur ria9781803237015_new
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Vendeur : Majestic Books, Hounslow, Royaume-Uni
Etat : New. Print on Demand. N° de réf. du vendeur 402170428
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Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
Etat : New. N° de réf. du vendeur 18395255273
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Build, train, and deploy large machine learning models at scale in various domains such as computational fluid dynamics, genomics, autonomous vehicles, and numerical optimization using Amazon SageMaker Key Features:Understand the need for high-performance computing (HPC) Build, train, and deploy large ML models with billions of parameters using Amazon SageMaker Learn best practices and architectures for implementing ML at scale using HPC Book Description: Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you'll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases. By the end of this book, you'll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle. What You Will Learn:Explore data management, storage, and fast networking for HPC applications Focus on the analysis and visualization of a large volume of data using Spark Train visual transformer models using SageMaker distributed training Deploy and manage ML models at scale on the cloud and at the edge Get to grips with performance optimization of ML models for low latency workloads Apply HPC to industry domains such as CFD, genomics, AV, and optimization Who this book is for: The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful. N° de réf. du vendeur 9781803237015
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Vendeur : moluna, Greven, Allemagne
Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. N° de réf. du vendeur 790818897
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Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
paperback. Etat : New. New. book. N° de réf. du vendeur ERICA80018032370156
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