Bridging the gap between foundation models and enterprise AI

Unlock a range of complex enterprise use cases by fine-tuning foundation models and by using them to build smaller, specialized deployable models with Snorkel Flow’s Data-centric Foundation Model Development.
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Data-centric Foundation Model Development

With Snorkel Flow’s Data-centric Foundation Model Development enterprise AI/ML teams overcome adaptation and deployment challenges currently blocking them from adopting foundation models to radically accelerate AI development.

Adapt

Build large, domain-specific training sets to fine-tune foundation models in minutes.

Auto-label

Automatically jumpstart training data labeling by distilling knowledge from foundation models.

Refine

Easily address mistakes foundation models make on your complex, domain-specific task.

Deploy

Build smaller, specialized models deployable within governance and cost controls.
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“With Snorkel Flow, we applied data-centric workflows to distill knowledge from foundation models and build high-cardinality classification models with more than 90% accuracy in days.”

Jackie Swansburg Paulino
CPO, Pixability

Supercharge enterprise AI with foundation models

Snorkel Flow’s unique programmatic labeling capabilities and data-centric development workflow give enterprises the tools they need to put foundation models to use for complex, performance-critical use cases.


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Foundation Model Fine-tuning


Create large, domain-specific training datasets to fine-tune and adapt foundation models for enterprise use cases with production-grade accuracy.

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Foundation Model Warm Start


Use foundation models with state-of-the-art zero- and few-shot learning to auto-label training data with a push of a button to train deployable models.

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Foundation Model Prompt Builder


Develop, evaluate, and combine prompts to tune and correct the output of foundation models to precisely label datasets and train deployable models.


Foundation Model Summit

January 17, 2023
Join us for a half-day summit bringing together perspectives on applying foundation models for enterprise use cases.
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Register now

Dive in

[get_press_posts]
Press
Blog
Research
Case studies
Press
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November 17, 2022
Snorkel AI Accelerates Foundation Model Adoption with Data-centric AI


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


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November 17, 2022
Snorkel dives into data labeling and foundation AI models


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July 28, 2022
Here’s why a gold rush of NLP startups is about to arrive


Blog
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November 17, 2022
Data-centric Foundation Model Development: Bridging the gap between foundation models and enterprise AI


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November 17, 2022
Better not bigger: How to get GPT-3 quality at 0.1% the cost


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November 3, 2022
Building an NLP application to analyze ESG factors in Earnings Calls using Snorkel Flow


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August 4, 2022
The Future of Data-Centric AI 2022 day 1 highlights


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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September 30, 2022
How Schlumberger uses Snorkel Flow to enhance proactive well management


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September 30, 2022
How a global custodial bank automated KYC verification with Snorkel Flow


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September 28, 2022
How Memorial Sloan Kettering Cancer Center used Snorkel Flow to scale clinical trial screening


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February 26, 2022
How Genentech extracted information for clinical trial analytics 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