Accelerate Deep Learning Workloads with Amazon SageMaker | Train, deploy, and scale deep learning models effectively using Amazon SageMaker
Lingua: inglese
Editore: Packt Publishing, 2022
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Da: preigu, Osnabrück, Germaniapreigu
Venditore AbeBooks dal 5 agosto 2024
Condizione: Nuovo
EUR 61,20
Quantità: 5 disponibili
Aggiungi al carrelloDescrizione dell’articolo da parte del venditore
Accelerate Deep Learning Workloads with Amazon SageMaker | Train, deploy, and scale deep learning models effectively using Amazon SageMaker | Vadim Dabravolski | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | Packt Publishing | EAN 9781801816441 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Codice articolo 125918307
- Titolo
- Accelerate Deep Learning Workloads with Amazon SageMaker | Train, deploy, and scale deep learning models effectively using Amazon SageMaker
- Autore
- Vadim Dabravolski
- Editore
- Packt Publishing
- Anno di pubblicazione
- 2022
- Condizione
- Neu
- Rilegatura
- Taschenbuch
- Lingua
- inglese
- ISBN 10
- 1801816441
- ISBN 13
- 9781801816441
- Peso dell'articolo
- 525 grammi
- Dimensioni
- 235 x 191 x 16 mm
- Cataloghi dei venditori
- Bücher
Plan and design model serving infrastructure to run and troubleshoot distributed deep learning training jobs for improved model performance.
Key Features
- Explore key Amazon SageMaker capabilities in the context of deep learning
- Train and deploy deep learning models using SageMaker managed capabilities and optimize your deep learning workloads
- Cover in detail the theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMaker
Book Description
Over the past 10 years, deep learning has grown from being an academic research field to seeing wide-scale adoption across multiple industries. Deep learning models demonstrate excellent results on a wide range of practical tasks, underpinning emerging fields such as virtual assistants, autonomous driving, and robotics. In this book, you will learn about the practical aspects of designing, building, and optimizing deep learning workloads on Amazon SageMaker. The book also provides end-to-end implementation examples for popular deep-learning tasks, such as computer vision and natural language processing. You will begin by exploring key Amazon SageMaker capabilities in the context of deep learning. Then, you will explore in detail the theoretical and practical aspects of training and hosting your deep learning models on Amazon SageMaker. You will learn how to train and serve deep learning models using popular open-source frameworks and understand the hardware and software options available for you on Amazon SageMaker. The book also covers various optimizations technique to improve the performance and cost characteristics of your deep learning workloads.
By the end of this book, you will be fluent in the software and hardware aspects of running deep learning workloads using Amazon SageMaker.
What you will learn
- Cover key capabilities of Amazon SageMaker relevant to deep learning workloads
- Organize SageMaker development environment
- Prepare and manage datasets for deep learning training
- Design, debug, and implement the efficient training of deep learning models
- Deploy, monitor, and optimize the serving of DL models
Who this book is for
This book is relevant for ML engineers who work on deep learning model development and training, and for Solutions Architects who design and optimize end-to-end deep learning workloads. It assumes familiarity with the Python ecosystem, principles of Machine Learning and Deep Learning, and basic knowledge of the AWS cloud.
Table of Contents
- Introducing Deep Learning with Amazon SageMaker
- Deep Learning Frameworks and Containers on SageMaker
- Managing SageMaker Development Environment
- Managing Deep Learning Datasets
- Considering Hardware for Deep Learning Training
- Engineering Distributed Training
- Operationalizing Deep Learning Training
- Considering Hardware For Inference
- Implementing Model Servers
- Operationalizing Inference Workloads
"Riassunto" può appartenere a un’altra edizione di questo titolo.
Informazioni sull’autore
Vadim Dabravolski is a Solutions Architect and Machine Learning Engineer. He has over 15 years of career in software engineering, specifically data engineering and machine learning. During his tenure in AWS, Vadim helped many organizations to migrate their existing ML workloads or engineer new workloads for the Amazon SageMaker platform. Vadim was involved in the development of Amazon SageMaker capabilities and adoption of them in practical scenarios. Currently, Vadim works as an ML engineer, focusing on training and deploying large NLP models. The areas of interest include engineering distributed model training and evaluation, complex model deployments use cases, and optimizing inference characteristics of DL models.
"Descrizione articolo" può appartenere a un’altra edizione di questo titolo.
preigu
Osnabrück, Germania
Venditore AbeBooks dal 5 agosto 2024
Tariffe di spedizione da Germania a U.S.A.
| Articolo | Da 60 a 60 giorni lavorativi | Da 60 a 60 giorni lavorativi |
|---|---|---|
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