Snorkel Drybell

One of the biggest bottlenecks in developing machine learning (ML) applications is the need for the large, labeled datasets used to train modern ML models. Creating these datasets involves the investment of significant time and expense, requiring annotators with the right expertise. Moreover, due to the evolution of real-world applications, labeled datasets often need to be thrown out or re-labeled.

https://ai.googleblog.com/2019/03/harnessing-organizational-knowledge-for.html

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