Adapt to real-world changes with a few clicks, not manual relabeling

Build and ship AI applications that are practical to adapt in response to inevitable production changes.

Respond easily to inevitable changes

Solve model degradation from data drift and reflect business goal changes with seamless label schema and labeling function updates.

Snorkel Flow regenerates your entire training set so you’re ready to retrain your model in minutes.



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Inspect and adapt across your pipeline

In Snorkel Flow you build more than a model. Use the visual builder to combine pre- and post-processing operators, models, and business logic.

Gain visibility into each step of your pipeline so you can, easily debug and experiment to optimize end-to-end application quality.



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Get from development to production

We make it simple to ship AI to production for real-world impact that you can maintain in the face of real-world changes.

Package applications into servable frameworks with a single click and deploy on the production infrastructure of your choice.

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Respond easily to inevitable changes

Solve model degradation, update label schemas, and reflect business goal changes with simple labeling function updates.

Snorkel Flow regenerates your entire training set so you’re ready to retrain your model in minutes.

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Platform adaptation capabilities


Production data analysis
Integrate with your production systems via API to spot data drift and take action in Snorkel Flow.
Flexible label schema
Adapt label schema and update applications with labeling function edits, not manual relabeling.
Data distribution adaptability
Receive guided suggestions to efficiently adapt ML applications to changing data distributions.
Granular inspection
Drill into specific slices for model performance and get clear guidance feedback to improve.
Standard export formats
Export applications as pre-packaged artifacts that can be deployed to any of the major model platforms.
Sandbox environments
Run sample data in a test environment to spot check performance of models in your pipeline.
Robust API integrations
Import production data from deployed models using REST APIs to analyze model performance.
Closed loop development
Inspect errors, iterate on training data, and retrain models within one platform.


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