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Automated Machine Learning in Practice: State of the Art and Recent Results

19 July 2019
Lukas Tuggener
Mohammadreza Amirian
Katharina Rombach
Stefan Lörwald
Anastasia Varlet
Christian Westermann
Thilo Stadelmann
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Abstract

A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions. Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so. This demand has given rise to a new research topic concerned with fitting machine learning models fully automatically - AutoML. This paper gives an overview of the state of the art in AutoML with a focus on practical applicability in a business context, and provides recent benchmark results on the most important AutoML algorithms.

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