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Designing Deep Learning Systems: A software engineer’s guide: A Guide for Software Engineers

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A vital guide to building the platforms and systems that bring deep learning models to production.

In Designing Deep Learning Systems you will learn how to:

Transfer your software development skills to deep learning systemsRecognize and solve common engineering challenges for deep learning systemsUnderstand the deep learning development cycleAutomate training for models in TensorFlow and PyTorchOptimize dataset management, training, model serving and hyperparameter tuningPick the right open-source project for your platform
Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning’s design requirements, Designing Deep Learning Systems is full of hands-on examples that will help you transfer your software development skills to creating these deep learning platforms. 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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. Th is book gives you that depth.

About the book

Designing Deep Learning Systems: A software engineer’s guide teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer’s perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you’ll need to build your own maintainable, efficient, and scalable deep learning platforms.

What’s inside

The deep learning development cycleAutomate training in TensorFlow and PyTorchDataset management, model serving, and hyperparameter tuningA hands-on deep learning lab
About the reader

For software developers and engineering-minded data scientists. Examples in Java and Python.

About the author

Chi Wang is a principal software developer in the Salesforce Einstein group. Donald Szeto was the co-founder and CTO of PredictionIO.

Table of Contents

1 An introduction to deep learning systems
2 Dataset management service
3 Model training service
4 Distributed training
5 Hyperparameter optimization service
6 Model serving design
7 Model serving in practice
8 Metadata and artifact store
9 Workflow orchestration
10 Path to production

Publisher ‏ : ‎ Manning Pubns Co
Publication date ‏ : ‎ 25 July 2023
Edition ‏ : ‎ 1st
Language ‏ : ‎ English
Print length ‏ : ‎ 337 pages
ISBN-10 ‏ : ‎ 1633439860
ISBN-13 ‏ : ‎ 978-1633439863
Item Weight ‏ : ‎ 612 g
Dimensions ‏ : ‎ 18.73 x 2.03 x 23.5 cm
Importer ‏ : ‎ Bookswagon, 2/13 Ansari Road, Daryaganj, New Delhi 110002, sales@bookswagon.com, 01140159253
Packer ‏ : ‎ Bookswagon, 2/13 Ansari Road, Daryaganj, New Delhi 110002, sales@bookswagon.com, 01140159253
Generic Name ‏ : ‎ Book
Best Sellers Rank: #95,542 in Books (See Top 100 in Books) #35 in API & Operating Environments #121 in Software Architecture #126 in Software Design & Engineering
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