AI beyond
manual labeling

Accelerate time to value with our transformative programmatic approach to data labeling and development.

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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).
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).

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Gartner Research:
Cool Vendors in AI Core
Technologies 2022

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

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Enterprise Tech 30:
by WingVC and Nasdaq

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Data50: The World’s Top Data Startups

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Madrona Venture Group and Goldman Sachs: Intelligent Applications Top 40

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The data-centric
AI development platform

Snorkel Flow gives you programmatic control of your data so you can create training data exponentially faster, iterate and adapt with ease, and ship more AI applications.

  • 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.

  • More to explore

    From integrated support for complex data and tasks to studios for building multi-model applications and dynamic domain expert partnership, Snorkel Flow offers more.

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“We accurately labeled a few thousand pathology reports
(95% accuracy, 85% precision) with one SME in days versus weeks.

It allows our teams to collaborate on the data accuracy and provides time efficiencies for our highly valued physicians and medical professionals.”

Janet Mak
Deputy CIO and VP of Digital Solutions

Collaboration Elevated

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.

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Structured data classification

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Sequence tagging

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Supported data types


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Conversational text
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Text documents
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Native PDFs

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HTML files

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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


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.

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Model training

Train custom models or choose from leading model frameworks with optional AutoML.

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Production serving

Deploy your models within Snorkel Flow or export to the service of your choice.

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Infrastructure

Host Snorkel Flow within the secure infrastructure of your choice.

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Dive in

[get_press_posts]
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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The Future of

Data-Centric AI


August 3-4, 2022 | Virtual

Register now