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.


[get_press_posts]
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?

Build high-quality AI 100x faster with Snorkel Flow, the AI data development platform.
Get started
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The Future of Data-Centric AI

June 7-8, 2023

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