Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
| Autor: | Chong Luo, Rahul Kuchhal, Braden Hancock, Houman Alborzi, Alexander Ratner, Daniel Rodriguez, Christopher Ré, Haidong Shao, Stephen H. Bach, Yintao Liu, Souvik Sen, Cassandra Xia, Rob Malkin |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science media_common.quotation_subject Industrial scale Machine Learning (stat.ML) 02 engineering and technology Data science Article Machine Learning (cs.LG) Order (business) Statistics - Machine Learning 020204 information systems Management system Scalability 0202 electrical engineering electronic engineering information engineering Production (economics) 020201 artificial intelligence & image processing Quality (business) Performance improvement media_common |
| Zdroj: | SIGMOD Conference |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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