使用Ballet框架实现协同数据科学开发
Enabling Collaborative Data Science Development with the Ballet Framework
摘要：
虽然开源软件开发模式已经成功地促成了构建软件系统的大规模合作，但数据科学项目通常由个人或小团队开发。我们描述了扩展数据科学协作的挑战，并提出了一个概念框架和ML编程模型来解决这些挑战。我们在Ballet中实例化了这些想法，Ballet是一个轻量级的协作框架，通过专注于特征工程和基于云的开发环境来实现开放源数据科学。使用我们的框架，合作者逐步提出的功能定义的存储库，每个进行ML性能评估，并可以自动合并到一个可执行的功能工程管道。我们利用芭蕾舞进行案例研究分析的收入预测问题与27个合作者，并讨论对未来的合作项目设计师的影响。
英文摘要：
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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