  Machine learning (ML) has become a vital part in many aspects of our daily
life. However, building well performing machine learning applications requires
highly specialized data scientists and domain experts. Automated machine
learning (AutoML) aims to reduce the demand for data scientists by enabling
domain experts to build machine learning applications automatically without
extensive knowledge of statistics and machine learning. This paper is a
combination of a survey on current AutoML methods and a benchmark of popular
AutoML frameworks on real data sets. Driven by the selected frameworks for
evaluation, we summarize and review important AutoML techniques and methods
concerning every step in building an ML pipeline. The selected AutoML
frameworks are evaluated on 137 data sets from established AutoML benchmark
suits.
