论文标题
评估从勘探到生产的无摩擦过渡的计算笔记本质量
Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production
论文作者
论文摘要
将数据驱动的AI功能集成到传统软件系统中的巨大趋势正在引起新的有趣挑战。这样的挑战之一是从机器学习项目的探索阶段进行平稳的过渡（数据科学家在实验室中构建了原型模型 - 其生产阶段 - 软件工程师将原型转换为可生产的AI组件。为了缩小这两个阶段之间的差距，可以通过合并合并的软件工程解决方案来改善数据科学家采用的工具和实践。特别是，计算笔记本在确定数据科学原型的质量中起着重要的作用。在我的研究项目中，我通过研究与计算笔记本协作的最佳实践并提出概念验证工具来促进准则的合规性，以应对这一挑战。
The massive trend of integrating data-driven AI capabilities into traditional software systems is rising new intriguing challenges. One of such challenges is achieving a smooth transition from the explorative phase of Machine Learning projects - in which data scientists build prototypical models in the lab - to their production phase - in which software engineers translate prototypes into production-ready AI components. To narrow down the gap between these two phases, tools and practices adopted by data scientists might be improved by incorporating consolidated software engineering solutions. In particular, computational notebooks have a prominent role in determining the quality of data science prototypes. In my research project, I address this challenge by studying the best practices for collaboration with computational notebooks and proposing proof-of-concept tools to foster guidelines compliance.
