Enabling Collaborative Data Science Development with the Ballet Framework

Abstract

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.CCS Concepts: • Human-centered computing → Collaborative and social computing systems and tools; Empirical studies in collaborative and social computing; • Computing methodologies → Machine learning; • Software and its engineering → Collaboration in software development.

Exploring Public Opinion on Responsible AI Through The Lens of Cultural Consensus Theory

“…Tools that support virtual and collaborative work during other phases of the data science workflow have shown promise, for example during feature engineering (Smith et al, 2017) or for the creation of labeled data (Reddi et al, 2021). A recent publication by Smith et al, 2021 shows that open-source software development practices can be used during feature engineering as part of a machine learning pipeline. Understanding how open data users collaborate virtually during data engineering will be essential to create workflows and tools that are better adapted to the challenges they face.…”