ArticleProceedings. ACM-SIGMOD International Conference on Management of Data
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale.
Article in Proceedings. ACM-SIGMOD International Conference on Management of Data. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 97 citations in OpenAlex.
- CAS: enhancing implicit constrained data augmentation with semantic enrichment for biomedical relation extraction and beyond.Database : the journal of biological databases and curation · 2025Article
- Biomedical relation extraction with knowledge base-refined weak supervision.Database : the journal of biological databases and curation · 2023Article
- Large-scale Vietnamese point-of-interest classification using weak labeling.Frontiers in artificial intelligence · 2022Article
- Cross-Modal Data Programming Enables Rapid Medical Machine Learning.Patterns (New York, N.Y.) · 2020Article
- Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset.Journal of digital imaging · 2020Article
- A Protein Interaction Information-based Generative Model for Enhancing Gene Clustering.Scientific reports · 2020Article
- Snorkel: rapid training data creation with weak supervision.The VLDB journal : very large data bases : a publication of the VLDB Endowment · 2020Article
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
Labeling training data is one of the most costly bottlenecks in developing machine learning-based applications. We present a first-of-its-kind study showing how existing knowledge resources from across an organization can be used as weak supervision in order to bring development time and cost down by an order of magnitude, and introduce Snorkel DryBell, a new weak supervision management system for this setting. Snorkel DryBell builds on the Snorkel framework, extending it in three critical aspects: flexible, template-based ingestion of diverse organizational knowledge, cross-feature production serving, and scalable, sampling-free execution. On three classification tasks at Google, we find that Snorkel DryBell creates classifiers of comparable quality to ones trained with tens of thousands of hand-labeled examples, converts non-servable organizational resources to servable models for an average 52% performance improvement, and executes over millions of data points in tens of minutes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.