Designing Deep Learning Systems: A software engineer's guide, Chi Wang, Donald Szeto

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.

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