Preface

With the growing interest in artificial intelligence (AI), there are millions of resources introducing various deep learning (DL) techniques for a wide range of problems. They might be sufficient to get you a data scientist position that many of your friends dream of. However, you will soon find out that the real difficulty with DL projects is not only selecting the right algorithm for the given problem but also efficiently preprocessing the necessary data in the right format and providing a stable service.

This book walks you through every step of a DL project. We start from a proof-of-concept model written in a notebook and transform the model into a service or application with the goal of maximizing user satisfaction upon deployment. Then, we use Amazon Web Services (AWS) to efficiently provide a stable service. Additionally, we look at how to monitor a system running a DL model after deployment, closing the loop completely.

Throughout the book, we focus on introducing various techniques that engineers at the frontier of the technology use daily to meet strict service specifications.

By the end of this book, you will have a broader understanding of the real difficulties in deploying DL applications at scale and will be able to overcome these challenges in the most efficient and effective way.