Train and analyze models to improve data quality and model performance

Use actionable, prescriptive analysis to iterate strategically and rapidly improve training data and models.
Request a demo

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Get immediate data
quality feedback


As you label your data, Snorkel Flow automatically retrains models to provide real-time analysis of both your model and (crucially) your training data quality.
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Improve with targeted iteration


Reach performance goals faster by focusing on the slices of your data that are most important to act on. Use auto-generated suggestions and active learning to iterate intelligently.

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Flexible model training


Train leading model architectures without code or use the Python SDK to train custom models on your own infrastructure and bring predictions back to Snorkel Flow for analysis.

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Modular, adaptable pipelines


In Snorkel Flow you have the flexibility to build more than a model. 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.


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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Platform labeling capabilities

Integrated model zoo

Train preconfigured, state-of-the-art ML models for a range of modeling tasks with a single click.

AutoML

Automate selecting the best algorithm and hyperparameters for your problem.

Guided iteration

Use suggested actions from error analysis to focus your efforts where you can make the greatest impact.

Custom model flexibility

Easily integrate with your existing models and inference infrastructure using our Python SDK.

Active learning

Use model guidance to prioritize programmatic labeling effort against the highest-impact slices of data.

Auto-generated visualizations

Understand model performance with auto-generated, interactive plots and analyses (no code needed).

Slice-based analyses

Identify, analyze, and improve data slices through collaboration workflows with domain experts.

Automated model updates

Get rapid feedback on training AI development from automatically retrained models and updated analyses.

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