  The massive trend of integrating data-driven AI capabilities into traditional
software systems is rising new intriguing challenges. One of such challenges is
achieving a smooth transition from the explorative phase of Machine Learning
projects - in which data scientists build prototypical models in the lab - to
their production phase - in which software engineers translate prototypes into
production-ready AI components. To narrow down the gap between these two
phases, tools and practices adopted by data scientists might be improved by
incorporating consolidated software engineering solutions. In particular,
computational notebooks have a prominent role in determining the quality of
data science prototypes. In my research project, I address this challenge by
studying the best practices for collaboration with computational notebooks and
proposing proof-of-concept tools to foster guidelines compliance.
