Label programmatically to break through manual labeling bottlenecks

Unblock development and deliver more AI value by dramatically accelerating training data labeling and iteration.

Maximize domain experts’ impact

Rather than asking domain experts to provide individual labels, distill their insight and intuition as labeling functions that are applied intelligently at scale.

Labeling functions can be simple or complex

Snorkel Flow can capture diverse sources of signal for labeling, including

  • Keywords, patterns, or phrases
  • Database lookups
  • External models
  • And more

Beyond brittle rule-based systems

Labeling functions (LFs) let you encode domain knowledge without worrying about every edge case. Snorkel Flow intelligently reconciles and applies imprecise and conflicting LFs in order to auto-label large training datasets in minutes

Platform labeling capabilities

Flexible labeling function creation
UI-based, custom code, and auto-suggested labeling functions to capture diverse sources of input.
Labeling functions from external models
Incorporate signal from integrated state-of-the-art foundation models with natural language prompts.
Auto-labeling
Snorkel Flow’s label model aggregates your labeling functions intelligently to produce training labels en masse.
Active learning
Use model guidance to prioritize programmatic labeling effort against the highest-impact slices of data.
Labeling functions from existing labels
Reuse existing labels (even noisy ones) as labeling functions that are combined and corrected by other labeling sources.
Instantaneous feedback
Get instant quantitative and qualitative feedback on the labeling functions you write for guided iteration.
Targeted slices
Comprehensive filtering options let you view and target specific slices of your data.
Diverse data displays
Multiple ways to view your data (individual, table, clusters, etc.) help you understand and label it more efficiently.


Supported data types


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Conversational text
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Text documents
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Native PDFs

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HTML files

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Semi-structured
tabular data

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Numeric data
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Network data
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And more




Labelling function suite

Capture labeling signal from diverse sources

Write labeling functions in the no-code UI or using the SDK to capture heuristics and resources across a range of complexity. Snorkel Flow combines and refines them to label at scale.

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Pattern matching

If a phrase like “send money” is in a email

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Third-party models

"If SentimentModel votes negative"

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Database lookups

If sender is in our Blocklist.db

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Legacy systems

If LegacySytem votes spam

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Heuristics

If SpellChecker finds 3+ spelling errors

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Crowd labels

If Worker #23 votes spam

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Boolean search

If unknown_sender AND foreign_source





Elevate collaboration with
domain experts

You rely on your domain experts and business partners for insight, expertise, and feedback. Snorkel Flow makes it easy to transfer knowledge, not just labels.

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Real-time progress sharing

Work in a single platform to remove the silos between domain experts, annotators, and data scientists.
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User-tailored workflows

Support for all teammates with both a comprehensive Python SDK and no-code interfaces.
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Efficient troubleshooting

Pinpoint data slices for domain expert spot-checks and troubleshooting to improve accuracy faster.
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Rich knowledge transfer

Gather context and insight beyond labels with patterns, tags, and comments.


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

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

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

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

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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