Snorkel AI
Case Studies
Georgetown University’s CSET Leverages Snorkel Flow for NLP Applications in Policy Research
Overview
Georgetown University’s CSET Leverages Snorkel Flow for NLP Applications in Policy ResearchSnorkel AI |
Sensors - Flow Meters Sensors - Liquid Detection Sensors | |
Cement Education | |
Product Research & Development Quality Assurance | |
Chatbots Machine Translation | |
Data Science Services Training | |
Operational Impact
The implementation of Snorkel Flow resulted in a significant improvement in the collaboration between data scientists and domain experts. The easy-to-use GUI for authoring labeling functions, along with the use of comments and tags for discussion and resolution of complex cases, made the process more efficient. The advanced labeling functions based on foundation-model embedding distances and clustering increased productivity. The guided error analysis and prioritized examples for targeted manual review using active learning reduced the time needed to adapt to evolving business criteria. This solution eliminated a lot of friction in data science and domain expert collaboration, bringing domain experts into the loop during the model development process, significantly improving project buy-in, knowledge transfer, and productivity. | |
Quantitative Benefit
Programmatically labeled 107K data points using advanced features | |
Achieved 85% precision on positive class, an eight percentage-point improvement over the previous solution | |
Significant reduction in labeling time | |