Anomaly Detection
Rapidly build high-accuracy, adaptive anomaly detection models over documents, logs, network traffic, and more, without hand-labeling training data using Snorkel Flow.
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Technology developed and deployed with the world’s leading organizations
Overview —
Detect Needles in the Data Stack
Rapidly build, adapt, and deploy agile anomaly detection applications.
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, and state-of-the-art anomaly detection model architectures to build high-accuracy anomaly detectors.
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.
Industry Use Cases —
Explore Enterprise Solutions For Anomaly Detection
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.
Case Study —
Stanford Medicine used Snorkel to detect medical anomalies using cross-modal data from radiographs, tomographs & monitoring devices, replacing months of hand labeling with few hours of programmatic data creation.
Problem
Labeling training data for triaging models took person-months to person-years of radiologist time.
Solution
Developed a cross-modal, anomaly detection application using Snorkel, matching or exceeding the performance of painstakingly gathered manual labels in hours.
Results
Currently being tested for deployment in Stanford Health & Department of Vetaran Affairs (VA) hospital systems.
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
For Developers
- Fully interoperable API and web UI
- Write custom operators with Python SDK
- Integrations to deploy models at scale
Resources —
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.