Collaboration challenges in building ML-enabled systems

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Abstract
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 face when building and deploying ML systems into production. We report on common collaboration points in the development of production ML systems for requirements, data, and integration, as well as corresponding team patterns and challenges. We find that most of these challenges center around communication, documentation, engineering, and process, and collect recommendations to address these challenges.
Funding Information
  • Natural Sciences and Engineering Research Council of Canada (NSERC) (RGPIN2021-03538)
  • NSF (1813598, 2131477)
  • Carnegie Mellon University (CMU)
  • Department of Defense (FA8702-15-D-0002)

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