Document classification

Build AI-powered document classification applications in a fraction of the time without hand-labeling data using Snorkel Flow.
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Data-centric AI technology developed at the Stanford AI Lab and proven at world-leading companies.

How Snorkel Flow works

One size fits you, not all

Achieve greater performance gains by exploiting domain-specific text features of your own data.
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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.

Document classification

Programmatically label training data across complex data types and build multi-model document classification applications with ease.
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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

Google

Google used Snorkel to replace 100K+ hand-annotated labels in critical ML pipelines for text classification.
Read more

Problem

Content, product, and event classification problems change too fast to hand-label, even with significant annotation budget.

Solution

Google deployed early versions of Snorkel's core technology with three high-impact teams, repurposing many resources as labeling functions.

Results

Hours of labeling function development replaced 10-100K+ hand labels, significantly impacting the bottom line and accelerating of ML adoption.

6 months

of hand-labeling data replaced in 30 mins

52%

performance improvement

100k+

hand labels replaced with a programmatic approach

Case Study

Google

Google used Snorkel to replace 100K+ hand-annotated labels in critical ML pipelines for text classification.
Read more

Problem

Content, product, and event classification problems change too fast to hand-label, even with significant annotation budget.

6 Months

of hand-labeling data replaced in 30 mins

Solution

Google deployed early versions of Snorkel's core technology with three high-impact teams, repurposing many resources as labeling functions.

52%

performance improvement

Results

Hours of labeling function development replaced 10-100K+ hand labels, significantly impacting the bottom line and accelerating of ML adoption.

100k+

hand labels replaced with programmatic approach

Case Study

Google

Google used Snorkel to replace 100K+ hand-annotated labels in critical ML pipelines for text classification.
Read More

Problem

Content, product, and event classification problems change too fast to hand-label, even with significant annotation budget.

Solution

Google deployed early versions of Snorkel's core technology with three high-impact teams, repurposing many resources as labeling functions.

Results

Hours of labeling function development replaced 10-100K+ hand labels, significantly impacting the bottom line and accelerating of ML adoption.

6 Months

of hand-labeling data replaced in 30 mins

52%

performance improvement

100k+

hand labels replaced with programmatic approach

Dive in

[get_press_posts]
Press
Blog
Research
Case studies
Press
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March 21, 2022
Snorkel AI welcomes industry leaders to the team
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August 9, 2021
This hot startup is now valued at $1 billion for its A.I. skills
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February 24, 2021
The Data-First Enterprise AI Revolution
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July 14, 2020
Meet The Stanford AI Lab Alums That Raised $15 Million To Optimize Machine Learning
View more press coverage
Blog
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February 4, 2022
Making Automated Data Labeling a Reality in Modern AI
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Date: Jan 25, 2022
The Principles of Data-Centric AI Development
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Date: Jan 5, 2022
Meet the Snorkelers
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Date: Jul 9, 2021
How to Use Snorkel to Build AI Applications
Read more blog content
Research
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2022
Universalizing Weak Supervision
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2021
Ontology-driven weak supervision for clinical entity classification in electronic health records
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2017
Rapid Training Data Creation with Weak Supervision
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2016
Data Programming: Creating Large Datasets Quickly
See more research
Customer Stories
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February 26, 2022
Genentech used Snorkel Flow to extract information from clinical trials
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February 18, 2022
Google used Snorkel to build and adapt content classification models
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2019
Intel used Snorkel to accelerate sales and marketing agents
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2019
Apple built a Snorkel-based system to answer billions of queries in multiple languages
Read more case studies
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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.
Request a demo