  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.
