Build AI-powered document classification applications in a fraction of the time without hand-labeling data using Snorkel Flow.
Technology developed and deployed with the world’s leading organizations
One Size Fits You, Not All
Achieve greater performance gains by exploiting domain-specific text features of your own data.
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.
Iterate on your application, using a closed-loop approach with intermediate results and analysis at every step to zero in on errors.
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.
Industry Use Cases —
Explore Enterprise Solutions For Classification
Build industry-specific AI applications combining state-of-the-art machine learning approaches with industry-specific best practices and last-mile connectors, all on an enterprise-scale platform.
Banks can classify contracts by terms and conditions to smoothly ensure regulatory complience.
TELECOM & CYBER
Telecom organizations can classify customer usage documents to target promotional offers.
Clinical Trial Matching
Biotech organizations can classify patient records to identify actionable clinical trial candidates.
Insurance underwriters can classify piolicy documents by behavioral or occupational variables to assess risk.
Search Engine Optimization
Software companies can recognize named entities in customer search queries and to optimize website content.
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.
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
Data Scientist Friendly
- Integrated Jupyter notebooks
- Guided error analysis
- Ready-to-use models
Domain Expert Friendly
- Intuitive, no-code UI
- Rich dashboards and visualizations
- Full-featured, push-button error analysis
- Platform access via Python SDK
- Online or batch API deployment
- Containerized software for cloud or on-premises deployments
Explore More About Snorkel
Learn more about groundbreaking techniques for programmatic labeling and weak supervision developed by Team Snorkel and the broader data science community.