Название: Software Engineer's Guide to Deep Learning System Design (MEAP v8)Автор: Chi Wang, Donald Szeto
Издательство: Manning Publications
Год: 2022
Страниц: 338
Язык: английский
Формат: pdf (true)
Размер: 29.1 MB
Design systems optimized for deep learning models. Written for software engineers, this book teaches you how to implement a maintainable platform for developing Deep Learning models.
Software Engineer's Guide to Deep Learning System Design is a practical guide for software engineers and data scientists who are designing and building platforms for Deep Learning (DL). It’s full of hands-on examples that will help you transfer your software development skills to implementing Deep Learning platforms.
In Software Engineer's Guide to Deep Learning System Design, you’ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting—and lucrative—career as a Deep Learning engineer.
The primary audience for this book is software engineers and data scientists who want to quickly transition into deep learning system engineering. To reinforce the understanding of the book content, we build a fully functioning sample Deep Learning system, from data management to training and model serving, and discuss its components chapter by chapter. You should have basic computer science knowledge to understand the technical material presented in this book. If you come from another background, you can still find non-technical material beneficial if you work with a deep learning system. We believe it is helpful for every team, especially managers, who use the system to have a general understanding of how it works, and we hope this book serves those readers as well.
After you finish reading the book, you will understand how Deep Learning systems are designed and put together. You will know when to gather requirements from the various teams using the system, and how to translate those requirements into system component design choices, as well as how to integrate components together to form a cohesive system that helps your users quickly develop and deliver Deep Learning features.
Because it's impossible to cover every possible Deep Learning system, environment and tool set, we decided to focus on principles. We believe there is a lot that can be accomplished with open source options, and provide suggestions for these alternatives in every chapter.
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