  The transition from AI/ML models to production-ready AI-based systems is a
challenge for both data scientists and software engineers. In this paper, we
report the results of a workshop conducted in a consulting company to
understand how this transition is perceived by practitioners. Starting from the
need for making AI experiments reproducible, the main themes that emerged are
related to the use of the Jupyter Notebook as the primary prototyping tool, and
the lack of support for software engineering best practices as well as data
science specific functionalities.
