Case Studies
Technology proven in production at some of the world’s leading organizations.
Problem
Solution
Results
Problem
Solution
Results
Apple
Apple built applications with an internal Snorkel-based system that answered billions of queries in multiple languages and processed trillions of records with up to 2.9x fewer errors.
Problem
Apple needed a system that supported engineers facing contradictory or incomplete supervision data.
Solution
Apple built a solution called Overton which utilized Snorkel’s framework of weak supervision to overcome cost, privacy, and cold-start issues.
Results
Overton achieved a 12%+ bump in F1 score by going from 30K to 1M data labels.
bump in F1 score
fewer errors with Snorkel-based applications
more labels generated
Intel used Snorkel to replace a high-cost, high-latency crowdsourcing pipeline and accelerate sales and marketing agents.
Problem
Solution
Deployed a proto version of Snorkel (Snorkel Osprey) to replace months-long crowdworker labels with cheap & fast programmatic labeling.
Results
Top Global Insurance
A global insurance provider built applications that classify and extract information from hundreds of thousands of websites using Snorkel Flow.
Problem
The insurance provider needed to classify and extract information from tens of thousands of complex websites to automate answering questions for downstream applications.
Solution
The insurance provider used Snorkel Flow to build applications with data science, developer, and subject matter expert teams working collaboratively.
Results
With Snorkel Flow, the team achieved 94% average accuracy in three person-weeks.
reduction in labeling costs
faster than hand-labeling
F1 score
Fortune 50 Bank
A Fortune 50 bank achieved a 25+ point performance gain over a black box vendor solution for news analytics application with Snorkel Flow- in just a few weeks.
Problem
The bank needed an accurate way to tag companies in unstructured news text, link them to identifiers (e.g., stock tickers), and classify mentions by sentiment and other aspects.
Solution
The bank used Snorkel Flow to develop an AI-powered news analytics application that monitors target companies' press coverage in unstructured data feeds.
Results
With Snorkel Flow, the team achieved a 25+ point performance gain over a legacy vendor system and internal heuristic approaches.
faster compared to hand-labeling
F1 score for news analytics application
point performance gain over black box vendor system
Researchers at Stanford Medicine used Snorkel to label medical imaging & monitoring datasets, replacing person-years of hand labeling with several hours of using Snorkel.
Problem
Solution
Results
Currently being tested for deployment in Stanford & Department of Veteran Affairs (VA) hospital systems.
Fortune 50 Bank
A Fortune 50 bank extracts financial information from PDFs with 99% accuracy in milliseconds using a financial spreading application built with Snorkel Flow.
Problem
The bank needed to extract structured financial data from balance sheets and income statements (hOCR PDF) from private company financials.
Solution
The bank used Snorkel Flow to develop an AI-powered financial spreading application that parses textual and spatial/visual data features.
Results
With Snorkel Flow, the team achieved superior performance with greater generalizability (2x coverage) compared to a purely rules-based approach.
coverage compared to rules-based approach
extraction accuracy
faster compared to hand-labeling