Labeling training data is one of the most costly bottlenecks in developing or modifying machine learning-based applications. We survey how resources from across an organization can be used as weak supervision sources for three classification tasks at Google, in order to bring development time and cost down by an order of magnitude. We build on the Snorkel framework, extending it as a new system, Snorkel DryBell, which integrates with Google's distributed production systems and enables engineers to develop and execute weak supervision strategies over millions of examples in less than thirty minutes. We find that Snorkel DryBell creates classifiers of comparable quality to ones trained using up to tens of thousands of hand-labeled examples, in part by leveraging organizational resources not servable in production which contribute an average 52% performance improvement to the weakly supervised classifiers.

本文通过研究发现，借助于企业现有的知识资源可以用作弱监督的数据，大大减少机器学习应用程序开发时间和成本，并介绍了一种新的弱监督管理系统“Snorkel DryBell”，该系统可以进行大规模的执行和提高分类性能。

Snorkel DryBell：在工业尺度上部署弱监督的案例研究