  In this paper, we illustrate how to enhance an existing state-of-the-art
modeling language and tool for the Internet of Things (IoT), called ThingML, to
support machine learning on the modeling level. To this aim, we extend the
Domain-Specific Language (DSL) of ThingML, as well as its code generation
framework. Our DSL allows one to define things, which are in charge of carrying
out data analytics. Further, our code generators can automatically produce the
complete implementation in Java and Python. The generated Python code is
responsible for data analytics and employs APIs of machine learning libraries,
such as Keras, Tensorflow and Scikit Learn. Our prototype is available as open
source software on Github.
