of hand labeled data replaced in 30 minutes using Snorkel
Overview
Faster, Lower-cost Development
Use programmatic labeling to develop high-quality AI applications in hours instead of spending weeks or months on expensive hand-labeling.
Higher-accuracy Models
Iterate on your application, using a closed-loop approach with intermediate results and analysis at every step to zero in on errors.
Flexible Integrations
Easily integrate labeling, training and analysis pipelines defined over diverse input types–text, PDF, HTML, and more–with downstream applications using APIs or a Python SDK.
Easier SME Collaboration
Build complex classification apps intuitively while preserving natural information about data taxonomies with subject matter expert (SME) collaboration.
Use Cases
Case Study
Google used Snorkel to replace 100K+ hand-annotated labels in critical ML pipelines for text classification.
Content, product, and event classification problems change too fast to hand-label, even with significant annotation budget.
Google deployed early versions of Snorkel's core technology with three high-impact teams, repurposing many resources as labeling functions.
Hours of labeling function development replaced 10-100K+ hand labels, significantly impacting the bottom line and accelerating of ML adoption.
6 MONTHS
of hand-labeling data replaced in 30 mins
performance improvement
hand labels replaced with
programmatic approach
An End-to-end ML Platform
Designed for Collaboration
Data Scientist Friendly
- Integrated Jupyter notebooks
- Instant analysis tools
- Ready-to-use models
Domain Expert Friendly
- Intuitive, no-code UI
- Rich dashboards and visualizations
- Full-featured, push-button error analysis
Developer Friendly
- Platform access via Python SDK
- Online or batch API deployment
- Containerized software for cloud or on-premises deployments
Research