Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

English | 2021 | ISBN: 978-1800567689 | 235 Pages | PDF, EPUB, MOBI | 175 MB

Follow a hands-on approach to AutoML implementation and associated methodologies and get to grips with automated machine learning
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 different ways of implementing AutoML in three major cloud service providers, including Microsoft Azure, Amazon Web Services (AWS), and the Google Cloud Platform. 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 lifecycle.
By the end of this book, you’ll be able to build and deploy automated machine learning models that are not only accurate, but also increase productivity, allow interoperability, and minimize featuring engineering tasks.
What you will learn

• Explore AutoML fundamentals, underlying methods, and techniques
• Assess AutoML aspects such as algorithm selection, auto featurization, and hyperparameter tuning in an
• applied scenario and differentiate between cloud and OSS offerings
• Implement AutoML in tools such as AWS, Azure, and GCP and while deploying ML models and pipelines
• Build explainable AutoML pipelines with transparency
• Understand automated feature engineering and time series forecasting
• Automate data science modeling tasks to implement ML solutions easily and focus on more complex problems