AI Applications —
Intelligent Apps, Customized With Your Data
Deploy powerful AI applications, designed to be customized by your teams using your data to solve your unique needs.
Improve performance by exploiting features unique to your data with custom classification apps
NAMED ENTITY RECOGNITION
Solve domain-specific syntactic and semantic challenges with precise NER apps
Collect useful text and data from virtually any table or form with flexible extraction apps
KNOWLEDGE BASE CONSTRUCTION
Populate relational databases with information extracted from documents and structured sources.
Case Study —
Google used Snorkel to replace 100K+ hand-annotated labels in critical ML pipelines.
Content, product, and event classification problems change too fast to hand-label, even with significant annotation budget.
We deployed early versions of Snorkel Flow's core technology with three high-impact teams at Google, repurposing many organizational resources as labeling functions.
Hours of labeling function development replaced 10-100K+ hand labels, significantly impacting the bottom line and acceleration of ML solution adoption.
of hand-labeling data replaced in 30 mins
To develop the first custom ML model
Accuracy for contract classification
hand labels replaced with programmatic approach
Contracts processed in minutes
An End-to-end ML Platform —
Designed for Collaboration
For Data Scientists
- Ready-to-use model zoo
- Auto-generated analysis tools
- Integrated Python notebooks
For Domain Experts
- Rich data annotation suite
- Intuitive, no-code labeling UI
- Model error analysis reports
- Fully interoperable API and web UI
- Write custom operators with Python SDK
- Integrations to deploy models at scale