Iterative development workflows for building AI applications
Speakers Bio:
Vincent Sunn Chen, Founding Engineer at Snorkel AI
Vincent is a Founding Engineer at Snorkel AI, where he leads the ML Engineering team. Previously, he researched ML systems at the Stanford AI Lab as a core contributor to the Snorkel open-source project, and he developed tools for the “data engine” on Tesla's Autopilot Vision team. Vincent earned his M.S. & B.S. in Computer Science from Stanford University.
Priyal Aggarwal, Machine Learning Engineer at Snorkel AI
Priyal Aggarwal is a Machine Learning Engineer at Snorkel AI. She recently graduated from Columbia University with a Master’s degree in Computer Science specializing in Machine Learning. Previously, she worked as a Software Engineer at Microsoft. She is a vocal supporter of women in tech and has held the position of Director for Women Who Code Delhi, received the Google Women Techmaker Scholarship and volunteered at GHC '20.
Abstract:
Modern AI application development is changing — rather than focusing solely on models trained over static datasets, practitioners are thinking more holistically about their pipelines, with a renewed emphasis on the training data. In this talk, we describe key interfaces and patterns for iteratively building high-quality AI applications using the Snorkel framework. We discuss how guided error analysis tools help developers prioritize the highest impact next step for improving quality— whether it's correcting supervision or fine-tuning models. Finally, we'll outline how these development workflows support collaboration with subject matter experts, who leverage domain expertise to impact end-to-end application quality.