  Machine learning (ML) is an increasingly important scientific tool supporting
decision making and knowledge generation in numerous fields. With this, it also
becomes more and more important that the results of ML experiments are
reproducible. Unfortunately, that often is not the case. Rather, ML, similar to
many other disciplines, faces a reproducibility crisis. In this paper, we
describe our goals and initial steps in supporting the end-to-end
reproducibility of ML pipelines. We investigate which factors beyond the
availability of source code and datasets influence reproducibility of ML
experiments. We propose ways to apply FAIR data practices to ML workflows. We
present our preliminary results on the role of our tool, ProvBook, in capturing
and comparing provenance of ML experiments and their reproducibility using
Jupyter Notebooks.
