Accelerate Deep Learning Workloads with Amazon SageMaker: Train, deploy, and scale deep learning models effectively using Amazon SageMaker
Author:   Vadim Dabravolski
Publisher:   Packt Publishing Limited
ISBN:  

9781801816441


Pages:   278
Publication Date:   28 October 2022
Format:   Paperback
Availability:   Available To Order   Availability explained
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Accelerate Deep Learning Workloads with Amazon SageMaker: Train, deploy, and scale deep learning models effectively using Amazon SageMaker


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Author:   Vadim Dabravolski
Publisher:   Packt Publishing Limited
Imprint:   Packt Publishing Limited
ISBN:  

9781801816441


ISBN 10:   1801816441
Pages:   278
Publication Date:   28 October 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

Table of Contents

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

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Author Information

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.

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