Solutions

Snorkel's technology powers AI-based solutions across a wide range of industries and use cases

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A new data-centric approach pioneered in the Stanford AI lab and proven with world-leading organizations.

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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

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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The Future of

Data-Centric AI


August 3-4, 2022 | Virtual

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