  Automated service composition as the process of creating new software in an
automated fashion has been studied in many different ways over the last decade.
However, the impact of automated service composition has been rather small as
its utility in real-world applications has not been demonstrated so far. This
paper presents \tool, an algorithm for automated service composition applied to
the area of machine learning. Empirically, we show that \tool is competitive
and sometimes beats algorithms that solve the same task but not benefit of the
advantages of a service model. Thereby, we present a real-world example that
demonstrates the utility of automated service composition in contrast to
non-service oriented solutions in the same area.
