Engineering Deep Learning Systems

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 p...