From research to production: building Snorkel Flow

Snorkel started as a research project at Stanford AI Lab in 2016. With data labeling being one of the most critical blockers for AI development, the Snorkel open source codebase gained popularity from the data science community and support from companies such as Google, Intel, Microsoft, and many more.

Today, Snorkel Flow, the data-centric AI platform powered by programmatic labeling, is changing how Fortune 500 enterprises not only label training data, but also build AI applications end-to-end.

In this webinar, Alex Ratner, co-founder and CEO, Snorkel AI, will discuss the basics of Snorkel and how it evolved from an academic research project and open source codebase to an enterprise-grade AI development platform. Alex will also be joined by Chris Glaze, Staff Research Scientist at Snorkel AI, to share his experience evaluating and using Snorkel Flow for Chubb, where he was the Lead Data Scientist.

The webinar will cover

• Basics of the Snorkel approach to programmatic labeling and weak supervision of ML model and a data-centric approach to AI development.
• A brief history of Snorkel’s start as an academic research project and open source codebase at the Stanford AI lab in 2016 and what led to its evolution into an enterprise-grade AI development platform, Snorkel Flow.
• Discussion with Chris Glaze about his experience evaluating Snorkel to solve critical business challenges using AI and adopting data-centric AI practice with Snorkel Flow at Chubb.