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All knowledge, from expert heuristics to foundation model insights, provides valuable labeling signal.
These inputs, which can be imprecise and conflict, are intelligently combined and applied at scale.
Real-time model training and analysis shows the quality of both labeling functions and data.
Guided iteration shows you where—and how—to improve, including many automated 1-click actions.


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Flexible labeling function creation

UI-based, custom code, and auto-suggested labeling functions to capture diverse sources of input.

Auto-labeling

Snorkel Flow’s label models aggregate your labeling functions intelligently to produce training labels en masse.

Labeling functions from external models

Incorporate signal from integrated state-of-the-art foundation models with natural language prompts.

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.

Annotator Suite

Interface for domain experts to label ground truth and troubleshoot challenging slices during iteration.

Instant feedback

Get real-time quantitative and qualitative feedback on the labeling functions you write for guided iteration.

Diverse data displays

Multiple ways to view your data (individual, table, clusters, etc.) help you understand and label it more efficiently.
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Press
Blog
Research
Case studies
Press
Snorkel + Snowflake
January 25, 2023
Snorkel AI partners with Snowflake to bring data-centric AI to the Snowflake Data Cloud


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January 18, 2023
Snorkel AI Heads Into 2023 With Record Momentum and Growth


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November 22, 2022
Deepening Snorkel AI’s partnership with Microsoft Azure AI


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November 17, 2022
AI startup Snorkel preps a new kind of expert for enterprise AI


Blog
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January 20, 2023
Unmasking Trafficking Risk in Commercial Sex Supply Chains with Machine Learning


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March 1, 2023
Combining foundation models with weak supervision


Scrabble tiles spelling "foundation." Relevant to Foundation Models, no?
March 1, 2023
Foundation models: a guide


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December 21, 2022
How programmatic labeling can minimize data exposure


Research
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2022
On the Opportunities and Risks of Foundation Models


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January 4, 2023
Ask Me Anything approach bolsters foundation models


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2022
A Survey on Programmatic Weak Supervision


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January 26, 2023
How Foundation Models bolster programmatic labeling


Customer Stories
Pixability logo for a Snorkel Flow customer success case study
January 11, 2023
How Pixability uses foundation models to accelerate NLP application development by months


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December 23, 2022
How a top 3 US bank used Snorkel Flow to automate 10-K review for their analysts


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December 19, 2022
How Georgetown University’s CSET uses Snorkel Flow to build NLP applications to inform policy research


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November 2, 2022
How a big four consulting firm used NLP to monitor news for audits with Snorkel Flow


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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
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The Future of Data-Centric AI

June 7-8, 2023

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