  Methods: This work introduces a method supporting the collaborative
definition of machine learning tasks by leveraging model-based engineering in
the formalization of the systems modeling language SysML. The method supports
the identification and integration of various data sources, the required
definition of semantic connections between data attributes, and the definition
of data processing steps within the machine learning support.
  Results: By consolidating the knowledge of domain and machine learning
experts, a powerful tool to describe machine learning tasks by formalizing
knowledge using the systems modeling language SysML is introduced. The method
is evaluated based on two use cases, i.e., a smart weather system that allows
to predict weather forecasts based on sensor data, and a waste prevention case
for 3D printer filament that cancels the printing if the intended result cannot
be achieved (image processing). Further, a user study is conducted to gather
insights of potential users regarding perceived workload and usability of the
elaborated method.
  Conclusion: Integrating machine learning-specific properties in systems
engineering techniques allows non-data scientists to understand formalized
knowledge and define specific aspects of a machine learning problem, document
knowledge on the data, and to further support data scientists to use the
formalized knowledge as input for an implementation using (semi-) automatic
code generation. In this respect, this work contributes by consolidating
knowledge from various domains and therefore, fosters the integration of
machine learning in industry by involving several stakeholders.
