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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…
Research recap: Ontology-driven weak supervision for clinical entity classification in electronic health records (EHRs) In this post, I have summarized the research published in this academic paper, Ontology-driven weak supervision for clinical entity classification in electronic health records by Jason Fries et al. This paper was published in Nature Communications in 2021.Problem statement Electronic health records (EHR) contain a rich…
Learning about the challenges and opportunities behind deep neural networks In this talk, Assistant Professor in Computer Science Sharon Li shares some exciting work about uncovering the unknowns of deep neural networks. She also shares some exciting challenges and opportunities in this domain. If you would like to watch Sharon’s presentation, we have included it below, or you can find…
The future of data-centric AI talk series In this talk, Assistant Professor of Biomedical Data Science at Stanford University, James Zou, discusses the work he and his team have been doing from a data-centric perspective to trustworthy and interpretable AI. If you would like to watch James’ presentation, we have included it below, or you can find the entire event…
The future of data-centric AI talk series Don’t miss the opportunity to gain an in-depth understanding of data-centric AI and learn best practices from real-world implementations. Connect with fellow data scientists, machine learning engineers, and AI leaders from academia and industry with over 30 virtual sessions. Save your seat at The Future of Data-Centric AI. Happening on August 3-4, 2022….
30+ sessions by 40+ speakers in 2 action-packed days Last year we organized The Future of Data-Centric AI conference to explore the shift from model-centric to data-centric AI. Speakers included researchers and industry experts such as Andrew Ng (Landing AI), Anima Anandkumar (NVIDIA), Chris Re (Stanford AI Lab), Michael DAndrea (Genentech), Skip McCormick (BNY Mellon), Imen Grida Ben Yahia (Orange)…
