of hand labeled data replaced in 30 minutes using Snorkel
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.
Banks can classify contracts by terms and conditions to smoothly ensure regulatory compliance.
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 policy documents by behavioral or occupational variables to assess risk.
Email Filtering and Routing
Software companies can classify emails to remove spam and route queries to the correct channels.
Social Media Analytics
Retailers can classify customer reviews to analyze and understand shopping behaviors.
Banks can classify client documents by credit-worthiness, streamlining the credit approval process.
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
hand labels replaced with
How Snorkel Flow Works
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
- Platform access via Python SDK
- Online or batch API deployment
- Containerized software for cloud or on-premises deployments
Train and You’ll Miss It: Interactive Model Iteration with Weak Supervision…M. Chen, et al, 2020
The Role of Massively Multi-Task and Weak Supervision in Software 2.0A. Ratner, et al, CIDR 2019
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial ScaleS. Bach, et al. SIGMOD 2019