Uber has numerous deep learning models, most of which are highly complex with many layers and a vast number of features. Understanding how these models work is challenging and demands significant resources to experiment with various training algorithms and feature sets. With ML explainability, the ML team aims to bring transparency to these models, helping to clarify their predictions and behavior. This transparency also assists the operations and legal teams in explaining the reasons behind specific prediction outcomes.
In this talk, Eric Wang will discuss the methods Uber used for explaining deep learning models and how we integrated these methods into the Uber AI Michelangelo ecosystem to support offline explaining.
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Eric (Qiushen) Wang is a software engineer at Uber’s Michelangelo team since 2020, focused on maintaining high ML quality across all models and pipelines. Prior to this, he contributed to Uber’s Marketplace Fares team from 2018 to 2020, developing fare systems for various services. Before that, he resided in Australia, and built a strong foundation in software engineering, working with notable companies including eBay, Qantas, and Equifax.