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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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How Snorkel Flow Works

Detect Needles in the Data Stack

Rapidly build, adapt, and deploy agile anomaly detection applications.
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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.
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Higher-accuracy Models

Iterate on your application, using a closed-loop approach with intermediate results and analysis at every step to zero in on errors.
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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.
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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 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.
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An End-to-End ML Platform

Designed for Collaboration

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For Data Scientists

  • Ready-to-use model zoo
  • Auto-generated analysis tools
  • Integrated Python notebooks
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For Domain Experts

  • Rich data annotation suite
  • Intuitive, no-code labeling UI
  • Model error analysis reports
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For Developers

  • Fully interoperable API and web UI
  • Write custom operators with Python SDK
  • Integrations to deploy models at scale
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Case Study

Stanford Medicine

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.

8 Months

Person-months of labeling replaced

94%

ROC AUC Performance

50K+

coverage percentage points
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Resources

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Are you ready to dive in?

Label data programmatically, train models efficiently, improve performance iteratively, and deploy applications rapidly—all in one platform.
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