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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

Designing Deep Learning Systems: A software engineer's guide 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.