We define and advance data and environments to push the AI frontier
building benchmarks and collaborating with
Featured research
Vision and impact
We help labs advance frontier models by working with domain experts to design and build complex, realistic datasets that drive model performance.
Benchmarking &
Evaluation
Build benchmarks that define and advance the AI frontier
Scaling Subject Matter Expertise
Define how subject matter experts encode their knowledge into data
RL, Training, & Data Valuation
Drive dataset development based on feedback from RL and model training
Community and open science
Open benchmarks, conversations, and research for real-world AI performance.
Open Benchmarks Grants
Backed by a $3M commitment, the program funds open-source datasets, benchmarks, and evaluation artifacts that shape how frontier AI systems are built and evaluated.
Bench Talks
Reading Group
DEEP RESEARCH Expertise
Technical advisors and distinguished affiliates
Browse research blogs and academic papers
The Future of Data-Centric AI Talk Series Background Snorkel co-founder Chris Ré is an associate professor of Computer Science at Stanford University and an award-winning researcher in data-based theory and machine learning. He has co-founded four companies based on his research in machine learning systems. Chris recently presented at the Future of Data-Centric AI virtual event in September, where he…
Machine Learning Whiteboard (MLW) Open-source Series We started our machine learning whiteboard (MLW) series earlier this year as an open-invite space to brainstorm ideas and discuss the latest papers, techniques, and workflows in the AI space. We emphasize an informal and open environment to everyone interested in learning about machine learning.In this episode, Fait Poms, a Ph.D. student at Stanford…
Main takeaways from The Future of Data-Centric AI Event We recently hosted The Future of Data-Centric AI, where academia, research, and industry experts and practitioners came together to discuss the shift from model-centric AI development to data-centric AI and what lies ahead. This post gives you a quick overview of the event and top takeaways from over eight hours of…
Many real-world ML deployments face the challenge of training a rare category model with a small labeling budget. In these settings, there is often access to large amounts of unlabeled data, therefore it is attractive to consider semisupervised or active learning approaches to reduce human labeling effort. However, prior approaches make two assumptions that do not often hold in practice;…
Defining and Building Malleable ML Systems – Machine Learning Whiteboard (MLW) Open-Source Series As you may know, earlier this year, we started our machine learning whiteboard (MLW) series, an open-invite space to brainstorm ideas and discuss the latest papers, techniques, and workflows in the AI space. We emphasize an informal and open environment to everyone interested in learning about machine learning. In this…
ScienceTalks with Paroma Varma In this episode of Science Talks, Snorkel AI’s Braden Hancock chats with Paroma Varma – a co-founder of Snorkel AI and one of the first and leading contributors to the Snorkel project. We discuss Paroma’s path into machine learning, her work in optimization and signal processing during her undergrad, weak supervision and image data during her…
For machine learning models trained with limited labeled training data, validation stands to become the main bottleneck to reducing overall annotation costs. We propose a statistical validation algorithm that accurately estimates the F-score of binary classifiers for rare categories, where finding relevant examples to evaluate on is particularly challenging. Our key insight is that simultaneous calibration and importance sampling enables…
Diving Into SliceLine – Machine Learning Whiteboard (MLW) Open-source Series Earlier this year, we started our machine learning whiteboard (MLW) series, an open-invite space to brainstorm ideas and discuss the latest papers, techniques, and workflows in the AI space. We emphasize an informal and open environment to everyone interested in learning about machine learning.In this episode, Kaushik Shivakumar dives into…
Objective: The change in performance of machine learning models over time as a result of temporal dataset shift is a barrier to machine learning-derived models facilitating decision-making in clinical practice. Our aim was to describe technical procedures used to preserve the performance of machine learning models in the presence of temporal dataset shifts. Methods: Studies were included if they were…
