  Many machine learning (ML) libraries are accessible online for ML
practitioners. Typical ML pipelines are complex and consist of a series of
steps, each of them invoking several ML libraries. In this demo paper, we
present ExeKGLib, a Python library that allows users with coding skills and
minimal ML knowledge to build ML pipelines. ExeKGLib relies on knowledge graphs
to improve the transparency and reusability of the built ML workflows, and to
ensure that they are executable. We demonstrate the usage of ExeKGLib and
compare it with conventional ML code to show its benefits.
