Enabling Collaborative Data Science Development with the Ballet Framework

• Published in: Proceedings of the ACM on human-computer interaction 2021-10, Vol.5 (CSCW2), p.1-39, Article 431
• Main Authors: Smith, Micah J., Cito, Jürgen, Lu, Kelvin, Veeramachaneni, Kalyan
• Format: Article
• Language: English
• Subjects: Collaboration in software development; Collaborative and social computing; Collaborative and social computing systems and tools; Computing methodologies; Empirical studies in collaborative and social computing; Human-centered computing; Machine learning; Software and its engineering; Software creation and management
• ISSN: 2573-0142; 2573-0142
• DOI: 10.1145/3479575

While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small teams. We describe challenges to scaling data science collaborations and present a conceptual framework and ML programming model to address them. We instantiate these ideas in Ballet, the first lightweight framework for collaborative, open-source data science through a focus on feature engineering, and an accompanying cloud-based development environment. Using our framework, collaborators incrementally propose feature definitions to a repository which are each subjected to software and ML performance validation and can be automatically merged into an executable feature engineering pipeline. We leverage Ballet to conduct a case study analysis of an income prediction problem with 27 collaborators, and discuss implications for future designers of collaborative projects.