Designing Deep Learning Systems: A software engineer's guide - Softcover

Wang, Chi; Szeto, Donald

Softcover

ISBN 10: 1633439860 ISBN 13: 9781633439863

Publisher: Manning, 2023

Synopsis

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 systems
• Recognize and solve common engineering challenges for deep learning systems
• Understand the deep learning development cycle
• Automate training for models in TensorFlow and PyTorch
• Optimize dataset management, training, model serving and hyperparameter tuning
• Pick 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 cycle
• Automate training in TensorFlow and PyTorch
• Dataset management, model serving, and hyperparameter tuning
• A 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

About this title

About the Author

Chi Wang is a principal software developer in the Salesforce Einstein group where he builds the deep learning platform for millions of Salesforce customers. Previously, he worked at Microsoft Bing and Azure on building large-scale distributed systems. Chi has filed six patents, mostly in deep learning systems.

Donald Szeto was the co-founder and CTO of PredictionIO, a startup that aimed to help democratize and accelerate the adoption of machine learning. PredictionIO was acquired by Salesforce, where he continued his work on machine learning and deep learning systems. Donald is currently investing in, advising, and mentoring technology startups.

From the Back Cover

Engineering Deep Learning Systems teaches you to design and implement an automated platform to support creating, training, and maintaining deep learning models. In it, you'll learn just enough about deep learning to understand the needs of the data scientists who will be using your system. You'll learn to gather requirements, translate them into system component design choices, and integrate those components into a cohesive whole. A complete example system and insightful exercises help you build an intuitive understanding of DL system design.

Edition details

• Publisher: Manning
• Publication date: 2023
• Language: English
• ISBN 10: 1633439860
• ISBN 13: 9781633439863
• Binding: Paperback
• Number of pages: 360