Bringing a Deep-Learning project into production at scale is quite challenging. To successfully scale your project, a foundational understanding of full stack Deep Learning, including the knowledge that lies at the intersection of hardware, software, data, and algorithms, is required. This book illustrates complex concepts of full stack Deep Learning and reinforces them through hands-on exercises to arm you with tools and techniques to scale your project. A scaling effort is only beneficial when it's effective and efficient. To that end, this guide explains the intricate concepts and techniques that will help you scale effectively and efficiently. Deep Learning and scaling are correlated. Deep Learning is capable of scaling your objectives from single task to multitask, from one modality to multimodality, from one class to thousands of classes. Anything is possible, provided you have scalable hardware and a large volume of data and write software that can efficiently scale to utilize all the resources available to you. This book aims to help you develop a deeper knowledge of the Deep Learning stack—specifically, how Deep Learning interfaces with hardware, software, and data. It assumes that the reader already has a fundamental knowledge of Deep Learning concepts such as optimizers, learning objectives and loss functions, and model assembly and compilation, as well as some experience with model development. Familiarity with Python and PyTorch is also essential for the practical sections of the book.