Enterprise ready, fully interoperable

We accelerate AI delivery for data science and machine learning teams at leading enterprises—including those with complex, highly sensitive data.


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Fully interoperable

Snorkel Flow is an integration-first platform that works with your existing ML stack seamlessly and securely. Build a data-centric workflow over your infrastructure, data sources, modeling libraries, serving environments, and more.

Python SDK

Pull in existing models, create custom labeling functions, and integrate with your existing data sources, applications, and MLOps infrastructure.

Use Snorkel Flow’s built-in notebooks, or install the Python SDK in other environments to build integrations.


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Flexible infrastructure

Choose your preferred deployment model whether that’s private cloud, public cloud, or Snorkel-hosted deployment.
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Flexible infrastructure

Choose your preferred deployment model whether that’s private cloud, public cloud, or Snorkel Flow Cloud.

Snorkel Flow is Kubernetes-native and tested across AWS, Azure, GCP, OpenShift, and more.

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The Snorkel Success Program

The AI/ML expertise of our success managers, engineers, and researchers is unparalleled.

We’ll work with you to enable your team and apply customer-proven best practices, maximizing your ROI and time to value.




Secure and compliant

We’re committed to upholding the security of our customers’ data, and Snorkel Flow is built around these principles. Please email security@snorkel.ai with any questions you may have.

Holistic security investments
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Regular third-party penetration testing

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Continuous vulnerability scanning

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Secure software development and employee training

Data access and protection
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Multi-provider single sign-on (SSO)

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Native role-based access controls

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Encryption in transit (TLS) and at rest (AES 256)





Bring everyone’s best to the table

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 model accuracy faster.
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Rich knowledge transfer

Gather context and insight beyond labels with patterns, tags, and comments.


Dive in

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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