  In this paper, we propose to adopt the MDE paradigm for the development of
Machine Learning (ML)-enabled software systems with a focus on the Internet of
Things (IoT) domain. We illustrate how two state-of-the-art open-source
modeling tools, namely MontiAnna and ML-Quadrat can be used for this purpose as
demonstrated through a case study. The case study illustrates using ML, in
particular deep Artificial Neural Networks (ANNs), for automated image
recognition of handwritten digits using the MNIST reference dataset, and
integrating the machine learning components into an IoT system. Subsequently,
we conduct a functional comparison of the two frameworks, setting out an
analysis base to include a broad range of design considerations, such as the
problem domain, methods for the ML integration into larger systems, and
supported ML methods, as well as topics of recent intense interest to the ML
community, such as AutoML and MLOps. Accordingly, this paper is focused on
elucidating the potential of the MDE approach in the ML domain. This supports
the ML engineer in developing the (ML/software) model rather than implementing
the code, and additionally enforces reusability and modularity of the design
through enabling the out-of-the-box integration of ML functionality as a
component of the IoT or cyber-physical systems.
