Engineering Deep Learning Systems you own this product

Chi Wang and Donald Szeto
  • MEAP began February 2022
  • Publication in Spring 2023 (estimated)
  • ISBN 9781633439863
  • 325 pages (estimated)
  • printed in black & white
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Look inside
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.

In Engineering 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
Engineering Deep Learning Systems is a practical guide for software engineers and data scientists who are designing and building platforms for deep learning. It’s full of hands-on examples that will help you transfer your software development skills to implementing 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

Behind every deep learning researcher is a team of engineers bringing their models to production. To build these systems, you need to understand how a deep learning system’s platform differs from other distributed systems. By mastering the core ideas in this book, you’ll be able to support deep learning systems in a way that’s fast, repeatable, and reliable.

about the book

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.

about the reader

For software developers interested in transitioning their skills to the field of deep learning system design. Also useful for engineering-minded data scientists who want to build more effective delivery pipelines. Examples in Java and Python.

about the authors

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.

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