Dabravolski Accelerate Deep Learning Workloads with Amazon SageMaker

Train, deploy, and scale deep learning models effectively using Amazon SageMaker
1. Auflage 2022
ISBN: 978-1-80181-311-2
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 278 Seiten

No detailed description available for "Accelerate Deep Learning Workloads with Amazon SageMaker".

Autoren/Hrsg.

Dabravolski, Vadim

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware

Inhaltsverzeichnis

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

Über Autor(innen)

Dabravolski Vadim:

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.