Automated Machine Learning Service Composition
This work addresses the utility gap in automated service composition for machine learning practitioners, though it appears incremental as it builds on existing service composition concepts.
The paper tackles the problem of automated service composition for machine learning, presenting an algorithm that is competitive and sometimes outperforms non-service-oriented solutions in real-world applications.
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.