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We help labs advance frontier models by working with domain experts to design and build complex, realistic datasets that drive model performance.
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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.
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Browse research blogs and academic papers
Snorkel AI co-founder and CEO Alex Ratner talks with Ananya Kumar about the work he did on improving the effectiveness of foundation models by using contrastive learning, image augmentations, and labeled subsamples.
Researcher Simran Arora tells Snorkel CEO Alex Ratner how she improved foundation model effectiveness by using “Ask Me Anything”-style questions.
More components in an ML lifecycle are designed to run on autopilot, but some tasks require human-in-the-loop ML, an active research topic that has seen an increasing number of publications in the last 10 years.
The recent debut of ChatGPT astounded the public with the power and speed of foundation models, but their enterprise use remains hampered by adaptation and deployment challenges. In the past year, Snorkel AI has researched several ways to overcome those challenges.
The Snorkel AI team will present five research papers advancing weak supervision and programmatic labeling at the NeurIPS 2022 conference that started this week.
Databricks’ Chief Technologist: Data-Centric AI can learn from Data Engineering and ML Engineering in five ways: continuous updates, versioning, code-centric deployment, data privatization and actionable monitoring.
This blog post introduces variants of Precision, Recall, and F1 metrics called Precision Gain, Recall Gain, and F1 Gain. The gain variants have desirable properties such as meaningful linear interpolation of PR curves and a universal baseline across tasks. This post explains what these benefits mean for you, how the gain metrics are calculated and outline some examples for intuitive comparison.
Snorkel AI just hosted the first day of The Future of Data-Centric AI conference 2022. This conference brings together data scientists, ML engineers, and AI leaders to share insights, best practices, and research on how to evolve the ML lifecycle from model-centric to data-centric approaches. This conference takes place over two days with 40+ sessions, 50+ speakers, and thousands of…
