Accelerate Deep Learning Workloads with Amazon SageMaker - Dabravolski, Vadim
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Learn to implement end-to-end deep learning on Amazon SageMaker with practical examples. Key Features:Explore key Amazon SageMaker capabilities in the context of deep learning Build, train and host DL models using SageMaker managed capabilities Cover in detail 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…mehr

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Produktbeschreibung
Learn to implement end-to-end deep learning on Amazon SageMaker with practical examples. Key Features:Explore key Amazon SageMaker capabilities in the context of deep learning Build, train and host DL models using SageMaker managed capabilities Cover in detail 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:Explore the 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 deep learning models Who this book is for: This book is written for deep learning and AI engineers who have a working knowledge of the Deep Learning domain and who wants to learn and gain practical experience in training and hosting DL models in the AWS cloud using Amazon SageMaker service capabilities.
Autorenporträt
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.