8 MONTHS

of hand labeling replaced in a few hours using Snorkel

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.

Use Cases

Explore Enterprise Solutions For Anomaly Detection

FINANCIAL SERVICES

Anti-Money Laundering
Banks can identify accounts with unusual deposit behavior for potential money laundering.

TELECOM & CYBER

Intrusion Detection
Cybersecurity firms can detect anomalous internet traffic and alert organizations of suspicious activity.

INSURANCE

Claims Fraud Detection
Insurance firms can monitor documents to identify potentially spurious claim behavior.

SOFTWARE

Traffic Monitoring
Software companies can detect periods with high or low website visits to understand the root cause.

RETAIL

Quality Assurance
E-commerce sites can detect incidents in logs and prevent the impact of lost revenue, or poor customer satisfaction scores.

HEALTHCARE

Condition Detection
Hospitals can detect anomalies in patient health records to find potentially problematic medical conditions.

FINANCIAL SERVICES

Fraud Detection
Banks can automatically identify and rapidly react to potentially fraudulent account behavior.

TELECOM & CYBER

Traffic Monitoring
Cybersecurity firms can identify networks with unusually high or low traffic and efficiently reallocate resources.

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.

Result

Currently being tested for deployment in Stanford Health & Department of Vetaran Affairs (VA) hospital systems.

8 MONTHS

of hand-labeling replaced by a few hours

94%

ROC AUC Performance

50K+

images labeled in minutes

How Snorkel Flow Works

Build Agile AI Apps for Anomaly Detection

Build agile anomaly detection applications that identify even rare occurrences in mountains of data. Eliminate onerous hand-annotation processes by programmatically labeling data with powerful labeling functions. Adjust data sampling to zero in on a rare class and augment data using class-aware tooling. Train state-of-the-art models and analyze performance using flexible tools for class-imbalanced problems. Deploy and integrate anomaly detection applications with other data tasks.

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

Based on Years of Novel Research

Learn more about groundbreaking techniques for machine learning and weak supervision developed by the Snorkel AI team at Stanford AI Lab and beyond.

RESEARCH

Weakly Supervised Classification of Aortic Valve Malformations Using …

J. Fries, et al. Nature Comms 2019

RESEARCH

The Role of Massively Multi-Task and Weak Supervision in Software 2.0

A. Ratner, et al, CIDR 2019

RESEARCH

Software 2.0 and Snorkel: Going Beyond Hand-Labeled Data

C. Ré, KDD 2018 (invited)

RESEARCH

Snorkel: Rapid Training Data Creation With Weak Supervision

A. Ratner, et al. VLDB 2018

BLOG

Snorkel AI:
Putting Data First in ML Development

Alex Ratner

RESEARCH

Cross-Modal Data Programming Enables Rapid Medical Machine Learning

J. Dunnmon, et al. Cell Patterns 2020