AI solutions are often blocked on one crucial ingredient — the massive labeled training datasets that fuel modern approaches. Snorkel Flow is a first-of-its-kind platform that focuses on a revolutionary new programmatic approach to labeling, building, and managing these training datasets, enabling a new level of rapid, iterative development and deployment of AI applications:
Label and build training data programmatically in hours without months of hand-labeling
Automatically clean, integrate, and manage programmatic training data from all sources
Train and deploy state-of-the-art machine learning models in-platform or via Python SDK
Analyze and monitor model performance to rapidly identify and correct error modes in the data
Telecommunications & Cyber
A top U.S. bank uses Snorkel Flow to quickly build AI applications that classify and extract information from their documents. On one recent time-sensitive use case, the bank had estimated over a month of hand-labeling to build a model. With Snorkel Flow, the team produced a solution that was over 99% accurate in under 24 hours, and that could be quickly and easily adapted to new problems and business lines.
Snorkel Flow Accuracy
From problem start
# Documents processed
Label and tag text, structured, semi-structured, and multi-modal data
Extract entities, relationships, and structured information from complex documents and forms
Rank content for relevance and other custom factors
Snorkel Flow productionizes years of research at the Stanford AI Lab — represented in 30+ research papers and co-developed with some of the world’s leading organizations — as well as on-going research in the broader community.