Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process. (arXiv:2110.10234v3 [cs.SE] UPDATED)

The introduction of machine learning (ML) components in software projects has
created the need for software engineers to collaborate with data scientists and
other specialists. While collaboration can always be challenging, ML introduces
additional challenges with its exploratory model development process,
additional skills and knowledge needed, difficulties testing ML systems, need
for continuous evolution and monitoring, and non-traditional quality
requirements such as fairness and explainability. Through interviews with 45
practitioners from 28 organizations, we identified key collaboration challenges
that teams …

arxiv building collaboration communication documentation engineering ml process systems