Preface

Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (AutoML) is to automatically set these hyperparameters to optimize performance. The latest deep neural networks have a wide range of hyperparameters for their architecture, regularization, and optimization, which can be customized effectively to save time and effort.

This book reviews the underlying techniques of automated feature engineering, model and hyperparameter tuning, gradient-based approaches, and more. You'll explore different ways of implementing these techniques in open source tools. Next, you'll focus on enterprise tools, learning about different ways of implementing AutoML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). As you progress, you'll explore the features of cloud AutoML platforms by building machine learning models using AutoML. Later chapters will show you how to develop accurate models by automating time-consuming and repetitive tasks involved in the machine learning development life cycle.

By the end of this book, you'll be able to build and deploy AutoML models that are not only accurate, but that also increase productivity, allow interoperability, and minimize featuring engineering tasks.