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Automated Machine Learning in Action you own this product

Qingquan Song, Haifeng Jin, and Xia Hu
  • MEAP began January 2021
  • Publication in Spring 2022 (estimated)
  • ISBN 9781617298059
  • 300 pages (estimated)
  • printed in black & white
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Automating automation itself is a new concept and this book does justice to it in terms of explaining the concepts, sharing real world advancements, use cases and research related to the topic.

Satej Kumar Sahu
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Optimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and Keras Tuner.

In Automated Machine Learning in Action you will learn how to:

  • Improve a machine learning model by automatically tuning its hyperparameters
  • Pick the optimal components for creating and improving your pipelines
  • Use AutoML toolkits such as AutoKeras and Keras Tuner
  • Design and implement search algorithms to find the best component for your ML task
  • Accelerate the AutoML process with data-parallel, model pretraining, and other techniques

Automated Machine Learning in Action reveals how premade machine learning components can automate time-consuming ML tasks. It’s written in a math-lite and accessible style, and filled with hands-on examples for applying AutoML techniques to every stage of a pipeline. You’ll use tools like AutoKeras to create pipelines that automatically select the best approach for your task, remove the burden of manual tuning, and can even be implemented by machine learning novices!

about the technology

Automated machine learning (AutoML) automates complex and time-consuming stages in a machine learning pipeline with prepackaged optimal solutions. This frees up data scientists from data processing and manual tuning, and lets domain experts easily apply machine learning models to their projects.

about the book

Automated Machine Learning in Action teaches you to automate your machine learning pipelines with AutoKeras and Keras Tuner. Written by the creators of the AutoKeras system, it’s full of AutoML techniques and advanced toolkits for optimizing how your machine learning models function.

AutoML concepts and techniques are introduced through real-world examples and practical code snippets—no complex math or formulas. You’ll quickly run through machine learning basics that open upon AutoML to non-data scientists, before putting AutoML into practice for image classification, supervised learning, and more. You’ll learn to automate selecting the best machine learning models or data preparation methods for your own machine learning tasks, so your pipelines tune themselves without needing constant input.

about the reader

For intermediate Python programmers who know the basics of machine learning.

about the author

Qingquan Song, Haifeng Jin, and Dr. Xia “Ben” Hu are the creators of the AutoKeras automated deep learning library. Qingquan and Haifeng are PhD students at Texas A&M University, and have both published papers at major data mining conferences and journals. Dr. Hu is an associate professor at Texas A&M University in the Department of Computer Science and Engineering, whose work has been utilized by TensorFlow, Apple, and Bing.

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A book with a lot of promise, covering a topic that's like to become hot in the next year or so. Read this now, and get ahead of the curve!

Richard Vaughan

A nice introduction to AutoML, its ambitions, and challenges both in theory and in practice.

Alain Couniot

Helps you to clearly understand the process of Machine Learning automation. The examples are clear, concise, and applicable to the real world.

Walter Alexander Mata López

The author's friendly style makes novices feel ready to try out AutoML tools.

Gaurav Kumar Leekha

A great book to take your machine learning skills to the next level.

Harsh Raval

An impressive effort by the authors to break down a complex ML topic into understandable chunks.

Venkatesh Rajagopal
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