  Data-driven engineering refers to systematic data collection and processing
using machine learning to improve engineering systems. Currently, the
implementation of data-driven engineering relies on fundamental data science
and software engineering skills. At the same time, model-based engineering is
gaining relevance for the engineering of complex systems. In previous work, a
model-based engineering approach integrating the formalization of machine
learning tasks using the general-purpose modeling language SysML is presented.
However, formalized machine learning tasks still require the implementation in
a specialized programming languages like Python. Therefore, this work aims to
facilitate the implementation of data-driven engineering in practice by
extending the previous work of formalizing machine learning tasks by
integrating model transformation to generate executable code. The method
focuses on the modifiability and maintainability of the model transformation so
that extensions and changes to the code generation can be integrated without
requiring modifications to the code generator. The presented method is
evaluated for feasibility in a case study to predict weather forecasts. Based
thereon, quality attributes of model transformations are assessed and
discussed. Results demonstrate the flexibility and the simplicity of the method
reducing efforts for implementation. Further, the work builds a theoretical
basis for standardizing data-driven engineering implementation in practice.
