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.

FINANCIAL SERVICES

Contract Intelligence

Banks can classify contracts by terms and conditions to smoothly ensure regulatory complience.

TELECOM & CYBER

Customer Segmentation

Telecom organizations can classify customer usage documents to target promotional offers.

HEALTHCARE

Clinical Trial Matching

Biotech organizations can classify patient records to identify actionable clinical trial candidates.

INSURANCE

Risk Classification

Insurance underwriters can classify policy documents by behavioral or occupational variables to assess risk.

SOFTWARE

Search Engine Optimization

Software companies can recognize named entities in customer search queries and to optimize website content.

RETAIL

Product Recommendation

E-commerce sites can recognize entities in product descriptions (price, key words, etc.) to improve recommender systems.

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.

Person-months of labeling replaced

<0hrs

To develop the first custom ML model

94%

ROC AUC Performance

+0%

Accuracy for contract classification

50K+

Images labeled in minutes

Contracts processed in minutes

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.