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.
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, a 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 an ML performance evaluation 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.
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model for software development
software development model
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groups
teams
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novel
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software
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,
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and a
through a focus on feature engineering, and an accompanying
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, with a plugin for collaborative feature engineering
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performance
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an extensive
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a real-world
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with 27 collaborators
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future designers of