Solving the Last Mile Problem of Foundation Models with Data-Centric AI //Alex Ratner // LLM in Prod

This LLMs in Production Conference section is proudly sponsored by Snorkel AI.

// Abstract
Today, large language or “foundation” models (FMs) represent one of the most powerful new ways to build AI models; however, they still struggle to achieve production-level accuracy out of the box on complex, high-value, and/or dynamic use cases, often “hallucinating” facts, propagating data biases, and misclassifying domain-specific edge cases. This “last mile” problem is always the hardest part of shipping real AI applications, especially in the enterprise- and while FMs provide powerful foundations, they do not “build the house”.

// Bio
Alex Ratner is the co-founder and CEO at Snorkel AI, and an Affiliate 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 defining and forwarding the concept of “data-centric AI”, the idea that labeling and developing data is the new center of the AI development workflow. His academic work focuses on data-centric AI and related topics in data management and statistical learning techniques, and applications to real-world problems in medicine, science, and more. Previously, he earned his A.B. in Physics from Harvard University.