Snorkel Flow is the only platform that enables organizations to build AI applications with a data-centric approach to AI, anchored on programmatic labeling that eliminates the need to manually label data which is notoriously expensive and inefficient

Snorkel AI, a data-centric AI platform company powered by programmatic data labeling, today announced the deployment of its flagship Snorkel Flow platform at Chubb, the world’s largest publicly traded property and casualty insurer with operations in 54 countries and territories.

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The Snorkel Flow solution adopted by Chubb accommodates noisy labels, labeling disagreements between subject-matter experts and a large number of classifications. Snorkel Flow also enables the management of several labelers and developed workflows efficiently with features such as reviews, reassignments and comments on one collaborative platform. Underpinned by a data-centric AI platform, Snorkel Flow also has the ability to unlock more use cases over time.

“Both at Stanford and now at Snorkel AI, we’ve built our platform and business around working with the world’s best AI-first organizations, supporting them in unlocking new value from AI while learning from them as well. We are so incredibly humbled to be partnered with sophisticated, forward-looking organizations in delivering real business value with cutting-edge AI development on Snorkel Flow. We are very pleased to be able to bring our solution to Chubb.”

AI is leading the shift in focus of AI practitioners from a model-centric to a data-centric approach. Snorkel Flow is the first data-centric AI platform that makes it feasible for data scientists and non-technical experts to build applications by programmatically labeling the data. In Snorkel Flow, users can label and manage data using code, train models and identify model error modes to iteratively improve them in a rapid, data-centric workflow, using both SDK and no-code interfaces. This shortens the development cycle and improves application quality significantly while being able to manage bias or any deterioration in production performance.

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