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. 
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                            A Meta-Summary of Challenges in Building Products with ML Components – Collecting Experiences from 4758+ Practitioners
                        
                    
    
            Incorporating machine learning (ML) components into software products raises new software-engineering challenges and exacerbates existing ones. Many researchers have invested significant effort in understanding the challenges of industry practitioners working on building products with ML components, through interviews and surveys with practitioners. With the intention to aggregate and present their collective findings, we conduct a meta-summary study: We collect 50 relevant papers that together interacted with over 4758 practitioners using guidelines for systematic literature reviews. We then collected, grouped, and organized the over 500 mentions of challenges within those papers. We highlight the most commonly reported challenges and hope this meta-summary will be a useful resource for the research community to prioritize research and education in this field. 
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                            - Award ID(s):
- 2131477
- PAR ID:
- 10444830
- Date Published:
- Journal Name:
- 2023 IEEE/ACM 2nd International Conference on AI Engineering – Software Engineering for AI (CAIN)
- Page Range / eLocation ID:
- 171 to 183
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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