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.

The paper “Automated Machine Learning in Practice: State of the Art and Recent Results” by CAI (InIT at time of publication) and PwC researchers 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.

The paper has drawn great interest and it meanwhile collected 80 citations (as of 11.10.2023), the largest number ever achieved by a publication at the IEEE Swiss Conference on Data Science (SDS). On the occasion of its tenth anniversary, the SDS has now honored this success with a Most Cited Award.