  Incorporating Machine Learning (ML) into existing systems is a demand that
has grown among several organizations. However, the development of ML-enabled
systems encompasses several social and technical challenges, which must be
addressed by actors with different fields of expertise working together. This
paper has the objective of understanding how to enhance the collaboration
between two key actors in building these systems: software engineers and data
scientists. We conducted two focus group sessions with experienced data
scientists and software engineers working on real-world ML-enabled systems to
assess the relevance of different recommendations for specific technical tasks.
Our research has found that collaboration between these actors is important for
effectively developing ML-enabled systems, especially when defining data access
and ML model deployment. Participants provided concrete examples of how
recommendations depicted in the literature can benefit collaboration during
different tasks. For example, defining clear responsibilities for each team
member and creating concise documentation can improve communication and overall
performance. Our study contributes to a better understanding of how to foster
effective collaboration between software engineers and data scientists creating
ML-enabled systems.
