Accelerate Deep Learning Workloads with Amazon SageMaker
Train, deploy, and scale deep learning models effectively using Amazon SageMaker
Vadim Dabravolski
Vadim Dabravolski
BIRMINGHAM—MUMBAI
Accelerate Deep Learning Workloads with Amazon SageMaker
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Contributors
About the Author
Vadim Dabravolski is a Solutions Architect and Machine Learning Engineer. He has had a career in software engineering for over 15 years, with a focus on 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 the adoption of them in practical scenarios.
Currently, Vadim works as an ML engineer, focusing on training and deploying large NLP models. His areas of interest include engineering distributed model training and evaluation, complex model deployment use cases, and optimizing inference characteristics of DL models.
About the reviewer
Brent Rabowsky is a manager and principal data science consultant at AWS, with over 10 years of experience in the field of ML. At AWS, he manages a team of data scientists and leverages his expertise to help AWS customers with their ML projects. Prior to AWS, Brent was on an ML and algorithms team at Amazon.com, and worked on conversational AI agents for a government contractor and a research institute. He also served as a technical reviewer of Data Science on AWS published by O’Reilly, and the following from Packt: Learn Amazon SageMaker, SageMaker Best Practices, and Getting Started with Amazon SageMaker Studio.