Advancing
data-centric AI
Snorkel AI is rooted in years of research into data-centric AI
development, including programmatic labeling, weak supervision,
and other state-of-the-art techniques.
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Technology represented in 60+ academic papers and funded by
Paradigm shift for machine learning
Snorkel’s technology has been used to unlock new ML use cases in healthcare, criminology, and journalism and to power mission critical AI applications for Fortune 500 enterprises such as Chubb, Genentech, Google, and more.
Snorkel is a fundamentally new interface to ML without hand-labeled training data
Mike Tamir
Chief ML Scientist, Head of Machine Learning/AI at Susquehanna International Group and Data Science Faculty, UC Berkeley
For many practical applications, it’s now more productive to hold the neural network architecture fixed, and instead find ways to improve the data.
Andrew Ng
Founder and CEO, Landing AI
Combining weak supervision and reinforcement learning enables Al systems to learn which actions can solve for which tasks. The result is a high-quality dataset and an optimized model.
Xuedong Huang
Technical Fellow and Azure AI CTO
Pioneering technology
SIGMOD
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale - S. Bach, et al, 2019
NATURE COMMS
Weakly Supervised Classification of Aortic Valve Malformations Using Unlabeled Cardiac MRI Sequences - J. Fries, et al, 2019
SIGMOD
Data Programming With DDLite: Putting Humans in a Different Part of the Loop - H. Ehrenberg, et al, 2016
NEURIPS
Learning to Compose Domain-Specific Transformations for Data Augmentation - A. Ratner, et al, 2017
VLDB
Snorkel: Rapid Training Data Creation With Weak Supervision - Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, Christopher Ré
Snorkel DryBell: Tackling content classification at Google
Snorkel was used to create classifiers of comparable quality to ones trained with tens of thousands of hand-labeled examples, convert non-servable organizational resources to servable models for an average 52% performance improvement, and execute over millions of data points in tens of minutes.
Published at SIGMOD’19
Collaboration with US FDA and Veterans Affairs on text and image data
Snorkel provided 132% average improvements to predictive performance over prior heuristic approaches and came within an average 3.6% of the predictive performance of large hand-curated training sets.
Published at VLDB’19
Snuba: Outperforming automated approaches
In collaborations with users at research labs, Stanford Hospital, and on open-source datasets, Snorkel outperformed other automated approaches like semi-supervised learning by up to 14.4 F1 points.
Published at VLDB’19
Slice-based learning for language and vision tasks
Snorkel improved over baselines in terms of slice-specific and
overall performance by up to 19.0 and 4.6 F1 points respectively on applications spanning natural language understanding and computer vision benchmarks as well as production-scale industrial systems.
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Cross-modal data programming
Snorkel yielded models that on average perform within 1.75 points and 10.3 ROC-AUC of those supervised with physician-years and -months of hand labeling respectively while using only person-days of developer time and clinician work—a time saving of 96%.
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Research at Snorkel AI
Our research team works closely with partners in academia and industry to make data-centric AI ubiquitous. The Snorkel AI team regularly publishes in academic journals, contributes to open source projects, applies research to the Snorkel Flow platform, and is faculty at the world's leading educational institutions.
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Label data programmatically, train models efficiently, improve performance iteratively, and deploy applications rapidly—all in one platform.
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