Accelerate Deep Learning Workloads with Amazon SageMaker
Train, deploy, and scale deep learning models effectively using Amazon SageMaker
Vadim Dabravolski
Accelerate Deep Learning Workloads with Amazon SageMaker
Train, deploy, and scale deep learning models effectively using Amazon SageMaker
Vadim Dabravolski
Paperback
|
English
€44.95
- From €15,- no shipping costs.
- 30 days to change your mind and return physical products
Description
Deep learning is one of the most cutting-edge fields in the AI space currently and most AI-powered applications currently utilize deep learning techniques. This book will teach you both software and hardware aspects used to run deep learning models at scale using Amazon SageMaker.
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.
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.