Every machine learning engineer deals with systems that have hyperparameters, and the most basic task in automated machine learning (Auto ML) 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 much more. You’ll discover different ways of implementing these techniques in open source tools and then learn to use enterprise tools for implementing Auto ML in three major cloud service providers: Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform. As you progress, you’ll explore the features of cloud Auto ML platforms by building machine learning models using Auto ML. The book will also show you how to develop accurate models by automating time-consuming and repetitive tasks in the machine learning development lifecycle.
By the end of this machine learning book, you’ll be able to build and deploy Auto ML models that are not only accurate, but also increase productivity, allow interoperability, and minimize feature engineering tasks.
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