of crowd-worker labels replaced in a fraction of time using Snorkel
Overview
Targeted Applications to Tackle Any Entity
Train custom, high-accuracy NER models on your data without hand-labeling.
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.
Rapidly Adaptable
Monitor for changes in the data, and rapidly adapt using built-in error analysis tools. Zoom in on errors to fine-tune training data & models with guided iteration.
High-accuracy Models
Leverage large amounts of labeled and unlabeled data, NLP primitives, and state-of-the-art model architectures to build high-accuracy models.
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.
Use Cases
NER Customized for Your Workflow
Case Study
Intel used Snorkel to replace a high-cost, high-latency crowdsourcing pipeline and accelerate sales & marketing agents.
Deployed Snorkel to replace months-long crowd-worker effort with cheap and fast template-based programmatic labeling.
Better performance and major cost savings for sales & marketing and customer analytics.
6 MONTHS
of crowd-worker labels replaced
+18.5
precision percentage points
+28.5
coverage percentage points
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