August 2023
Intermediate to advanced
360 pages
11h 7m
English
Overview
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:
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
About the Reader
For software developers and engineering-minded data scientists. Examples in Java and Python.
About the Authors
Chi Wang is a principal software developer in the Salesforce Einstein group. Donald Szeto was the co-founder and CTO of PredictionIO.
Quotes
Read it once to get the big picture and then return to it again and again when building systems, designing components, and making crucial choices to satisfy all the teams that use them.
- From the Foreword by Silvio Savarese and Caiming Xiong, Salesforce
Written by true industry experts. Their insights are invaluable for software engineers looking to design and implement maintainable platforms for DL model development that meet the highest standards of efficiency and scalability.
- Simon Chan, Firsthand Alliance
Invaluable and timely insights for teams expanding their DL systems. This book anticipates the needs of a diverse set of organizations, and its content can be easily tailored to your current situation or your personal interests.
- Weiping Peng, Airbnb
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 Authors
Chi Wang is a principal software developer in the Salesforce Einstein group. Donald Szeto was the co-founder and CTO of PredictionIO.
Quotes
Read it once to get the big picture and then return to it again and again when building systems, designing components, and making crucial choices to satisfy all the teams that use them.
- From the Foreword by Silvio Savarese and Caiming Xiong, Salesforce
Written by true industry experts. Their insights are invaluable for software engineers looking to design and implement maintainable platforms for DL model development that meet the highest standards of efficiency and scalability.
- Simon Chan, Firsthand Alliance
Invaluable and timely insights for teams expanding their DL systems. This book anticipates the needs of a diverse set of organizations, and its content can be easily tailored to your current situation or your personal interests.
- Weiping Peng, Airbnb
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