The Snorkel project started at Stanford in 2016 with a simple technical bet: that it would increasingly be the training data, not the models, algorithms, or infrastructure, that decided whether a machine learning project succeeded or failed. Given this premise the creators started by empowering users to programmatically label, build, and manage training data.
Snorkel is a system for programmatically building and managing training datasets. In Snorkel, users can develop training datasets in hours or days rather than hand-labeling them over weeks or months.
Snorkel currently exposes three key programmatic operations: labeling data, for example using heuristic rules or distant supervision techniques; transforming data, for example rotating or stretching images to perform data augmentation; and slicing data into different critical subsets. Snorkel then automatically models, cleans, and integrates the resulting training data using novel, theoretically-grounded techniques.
Snorkel has been deployed in industry, medicine, science, and government to build new ML applications in a fraction of the time;
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