As a case study, the problem domain of automated machine learning has been considered where the aim is to choose algorithms and their parameters tailored to the user's data. Transferred to the OTF Computing context, the challenge is to configure a service composition tailored to the customer's needs. More specifically, it means that the OTF Provider aims at the provision of a service composition of machine learning basic services that generalizes well on the data provided by the customer. The case study is an interesting scenario because the associated search space of the possible service compositions is of complex structure, extremely large (>1040), the evaluation of dynamic non-functional properties can be done automatically and the measured properties are of particular importance to the customer. Furthermore, dynamich non-functional properties, e.g. classification accuracy, cannot be determined for new data unless the service composition candidates are executed. This case study has also been used as a running example in the Proof-of-Concept (PoC) [link].