AI beyond manual labeling
now supercharged by foundation models

Accelerate time to value with our transformative approach to data-centric AI—powered by programmatic labeling and now, foundation models.

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Pioneered at the Stanford AI lab and proven with world-leading organizations.

Introducing Data-centric Foundation Model Development

New Snorkel Flow capabilities for enterprises to unlock complex, performance-critical use cases with GPT-3, RoBERTa, T5, and other foundation models.

Pixability distilled knowledge from foundation models and built smaller classification models with more than 90% accuracy in days with Data-centric Foundation Model Development in Snorkel Flow.

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Snorkel Flow's data-centric, programmatic workflow speeds AI development by 10-100x.

Label faster

Current problem

AI requires large, high-quality training data sets, but labeling by hand is slow and expensive. Too many AI projects never get off the ground.

How it works

Label massive training data sets in minutes, not months. Write labeling functions to programmatically capture human insight and existing resources.

Snorkel Flow then applies and intelligently aggregates these labeling functions to auto-label millions of data points at computer speed.

Model faster

Current problem

Model errors that delay production often originate from the training data. Fixing the data is slow and difficult—and you’re flying blind as to what corrections to make.

Snorkel Flow solution

As you label training data, Snorkel Flow trains models in real-time, providing actionable guidance to get to production-grade performance.

With programmatic labeling, data iteration is remarkably efficient: simply edit or add labeling functions to address errors.

Adapt faster

Current problem

Once deployed, it’s difficult to adapt applications to real-world data drift or objective changes. Maintaining models can require complete manual relabeling.

Snorkel Flow solution

Package for production with a click, then adapt applications quickly with simple edits to label schema and labeling functions.

Snorkel Flow regenerates your entire training set so you’re ready to retrain your model in minutes (and stay in production).

Label faster

Model faster

Adapt faster

Current problem

AI requires large, high-quality training data sets, but labeling by hand is slow and expensive. Too many AI projects never get off the ground.

Snorkel Flow solution

Label massive training data sets in minutes, not months. Write labeling functions to programmatically capture human insight and existing resources.

Snorkel Flow then applies and intelligently aggregates these labeling functions to auto-label millions of data points at computer speed.

Current problem

Model errors that delay production often originate from the training data. Fixing the data is slow and difficult—and you’re flying blind as to what corrections to make.

Snorkel Flow solution

With programmatic labeling, data iteration is efficient: simply edit or add labeling functions to fix errors and speed time-to-performance.

As you label training data, Snorkel Flow trains models in real-time, providing actionable guidance to get to production-grade performance.

Current problem

Once deployed, it’s difficult to adapt applications to real-world data drift or objective changes. Maintaining models can require complete manual relabeling.

Snorkel Flow solution

Package for production with a click, then adapt applications quickly with simple edits to label schema and labeling functions.

Snorkel Flow regenerates your entire training set so you’re ready to retrain your model in minutes (and stay in production).

Gartner Research:
Cool Vendors in AI Core
Technologies 2022

CB Insights AI 100:
The most promising artificial
intelligence startups of 2022

Enterprise Tech 30:
by WingVC and Nasdaq

Data50: The World’s Top Data Startups

Madrona Venture Group and Goldman Sachs: Intelligent Applications Top 40

The data-centric
AI development platform

Snorkel Flow gives you maximum data utility so you can create training data exponentially faster, iterate and adapt with ease, and ship more AI applications.

Use foundation models

Supercharge AI development by adapting foundation models to your domain faster than ever, and by encoding domain-specific foundation model knowledge into smaller, high-accuracy deployable production models.
Label programmatically

Easily create labeling functions rather than labeling data points one-by-one. Snorkel Flow uses these to auto-label vast training datasets in minutes.
Model instantly

Snorkel Flow continuously trains and analyzes models to guide targeted iteration. You can also use the Python SDK to train custom models.
Iterate rapidly

Go from analysis to action and reach performance goals quickly with pre-scriptive guidance to iterate on both models and data.

Learn more about Snorkel Flow

Deep platform support for complex ML tasks and data types

Build powerful AI solutions to handle real-world tasks over complex data types. Combine ML tasks for multi-model applications across a range of complex data types and formats.

Document classification

Information extraction

Structured data classification

Sequence tagging

Conversation analysis

Explore more solutions

Supported data types

Conversational text

Text documents

Native PDFs

HTML files

Semi-structured/ tabular data

Numeric data

Network data

And more

Healthcare

Banking

Insurance

Government

Get more from domain expert partnership.

Don't limit your subject matter experts' participation to tediously labeling one by one. Empower them to transfer their knowledge in a fraction of the time required by manual labeling. Activate their expertise for dramatically better training data creation, iteration, and troubleshooting.

Enterprise ready, fully interoperable

Cloud-agnostic and fully interoperable with your existing ML stack via an extensive Python SDK and other endpoints. Snorkel Flow provides enterprise-grade security and governance, user-tailored workflows, and access to unparalleled expertise.

Data ingest

Quickly and securely integrate to data pipelines or upload data locally.