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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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