Abstract: Data-centric AI broadly describes the idea that *data*, rather than models, is increasingly the crux of success or failure in AI for many settings and use cases. More specifically, data-centric AI defines ML development workflows that center around principally iterating on the *training data*–e.g. labeling, sampling, slicing, augmenting, etc.–rather than the model architecture. In this talk, I'll describe how programmatic or weak supervision can not only facilitate these data-centric workflows (in ways that manual labeling cannot), but more importantly, will present an overview about how it can serve as an API for rich organizational knowledge sources, presenting recent technical results and user case studies.
Bio: Alex Ratner is the co-founder and CEO at Snorkel AI, and an Assistant Professor of Computer Science at the University of Washington. Prior to Snorkel AI and UW, he completed his Ph.D. in CS advised by Christopher Ré at Stanford, where he started and led the Snorkel open source project, and where his research focused on applying data management and statistical learning techniques to emerging machine learning workflows such as creating and managing training data and applying this to real-world problems in medicine, knowledge base construction, and more. Previously, he earned his A.B. in Physics from Harvard University.