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Methods: This work introduces a method supporting the collaborativedefinition of machine learning tasks by leveraging model-based engineering inthe formalization of the systems modeling language SysML. The method supportsthe identification and integration of various data sources, the requireddefinition of semantic connections between data attributes, and the definitionof data processing steps within the machine learning support.  Results: By consolidating the knowledge of domain and machine learningexperts, a powerful tool to describe machine learning tasks by formalizingknowledge using the systems modeling language SysML is introduced. The methodis evaluated based on two use cases, i.e., a smart weather system that allowsto predict weather forecasts based on sensor data, and a waste prevention casefor 3D printer filament that cancels the printing if the intended result cannotbe achieved (image processing). Further, a user study is conducted to gatherinsights of potential users regarding perceived workload and usability of theelaborated method.  Conclusion: Integrating machine learning-specific properties in systemsengineering techniques allows non-data scientists to understand formalizedknowledge and define specific aspects of a machine learning problem, documentknowledge on the data, and to further support data scientists to use theformalized knowledge as input for an implementation using (semi-) automaticcode generation. In this respect, this work contributes by consolidatingknowledge from various domains and therefore, fosters the integration ofmachine learning in industry by involving several stakeholders.

Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML