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Due to the increase in digitalization Machine Learning (ML)algorithms bare high potentials for process optimization in the production quality-domain. Nowadays, ML-algorithms are hardly implemented in the production environment. In this paper, we present a tangible use case in which MLalgorithms are applied for predicting the quality of products in a process chain and present the lessons learned we extracted from the application. In the described project, the process of choosing ML-algorithms was a bottleneck. Therefore we describe a promising approach how a decision making tool can help selecting ML-algorithms problem-specifically.

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