Enabling collaborative data science development with the Ballet framework

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