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:
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.
- 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
- ISBN10 1633439860
- ISBN13 9781633439863
- Publish Date 6 July 2023
- Publish Status Active
- Publish Country US
- Imprint Manning Publications
- Format Paperback (US Trade)
- Pages 325
- Language English