Large-Scale Embedding Feature Generation at Uber

Speaker: Dhruva Dixith Kurra

Embeddings are integral to numerous top-tier models at Uber, driving critical machine learning (ML) systems such as UberEats, HomeFeed, and Ads platforms. This talk will provide an in-depth exploration of how embeddings are generated at scale for various entities, such as eaters and restaurants. These embeddings are extensively utilized as features in downstream critical models and nearest neighbor-based retrieval systems.  We will discuss the entire lifecycle of embeddings, from creation to deployment, and essential aspects such as versioning, analytics, and monitoring, which ensure the safe and consistent usage of embeddings in both offline and online environments.