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Labeling training data is one of the most costly bottlenecks in developingmachine learning-based applications. We present a first-of-its-kind studyshowing how existing knowledge resources from across an organization can beused as weak supervision in order to bring development time and cost down by anorder of magnitude, and introduce Snorkel DryBell, a new weak supervisionmanagement system for this setting. Snorkel DryBell builds on the Snorkelframework, extending it in three critical aspects: flexible, template-basedingestion of diverse organizational knowledge, cross-feature productionserving, and scalable, sampling-free execution. On three classification tasksat Google, we find that Snorkel DryBell creates classifiers of comparablequality to ones trained with tens of thousands of hand-labeled examples,converts non-servable organizational resources to servable models for anaverage 52% performance improvement, and executes over millions of data pointsin tens of minutes.

Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale