Automate data labeling to break through manual labeling bottlenecks

Unblock development and deliver more AI value by dramatically accelerating training data labeling and iteration.
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Programmatic labeling accelerates training data creation 10-100x.

Deliver high-quality training data that’s more explainable and adaptable in minutes, not months.

Encode

All knowledge, from expert heuristics to foundation model insights, provides valuable labeling signal.

Combine

These inputs, which can be imprecise and conflict, are intelligently combined and applied at scale.

Measure

Real-time model training and analysis shows the quality of both labeling functions and data.

Improve

Guided iteration shows you where—and how—to improve, including many automated 1-click actions.

Capture labeling signal from diverse sources


Write labeling functions in the no-code UI or using the SDK to capture heuristics and resources across a range of complexity. Snorkel Flow combines and refines them to label at scale.
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Native support for complex data types


Speed development even when data is messy, unstructured, or highly variable. Use pre- and post-processors, data viewers, and LF types purpose-built for text, PDFs, conversational data, and more.
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Conversational text

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Text documents
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Native PDFs
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HTML pages
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Semi-structured/ tabular data
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Numeric data
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Network data
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And more

Adapt easily to inevitable changes


Keep models performant in the face of data drift or objective 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.



Elevate collaboration with
domain experts

You rely on your domain experts and business partners for insight, expertise, and feedback. Snorkel Flow makes it easy to transfer knowledge, not just labels.

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Real-time progress sharing

Work in a single platform to remove the silos between domain experts, annotators, and data scientists.
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User-tailored workflows

Support for all teammates with both a comprehensive Python SDK and no-code interfaces.
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Efficient troubleshooting

Pinpoint data slices for domain expert spot-checks and troubleshooting to improve accuracy faster.
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Rich knowledge transfer

Encode the rationale behind labeling decisions with labeling functions which are inspectable, adaptable, and reusable.


Platform labeling capabilities

Flexible labeling function creation

UI-based, custom code, and auto-suggested labeling functions to capture diverse sources of input.

Auto-labeling

Snorkel Flow’s label models aggregate your labeling functions intelligently to produce training labels en masse.

Labeling functions from external models

Incorporate signal from integrated state-of-the-art foundation models with natural language prompts.

Active learning

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

Labeling functions from existing labels

Reuse existing labels (even noisy ones) as labeling functions that are combined and corrected by other labeling sources.

Annotator Suite

Interface for domain experts to label ground truth and troubleshoot challenging slices during iteration.

Instant feedback

Get real-time quantitative and qualitative feedback on the labeling functions you write for guided iteration.

Diverse data displays

Multiple ways to view your data (individual, table, clusters, etc.) help you understand and label it more efficiently.

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