Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber

Lezhi Li, Yunfeng Bai, and Yang Wang, Uber Inc.

Abstract: 

Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, Uber’s in-house model-agnostic visualization tool for ML performance diagnosis and model debugging. Manifold utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. We demonstrate current applications of the Manifold on the classification and regression tasks at Uber and discuss other potential machine learning use scenarios where Manifold can be applied.

Yang Wang, Uber Inc.

Yang Wang is a Sr. Research Engineer leading the Machine Learning Visualization team at Uber. His research interests lie in Human-Computer Interaction and High-Performance Computing, specifically, methodologies and systems to model the Interpretability and Actionability of AI-aided decision-making processes. At Uber, Yang and team build ML infrastructures, publish & tech-transfer research papers, and work across business units to help Data Scientists, Engineers, and City-Ops accelerate their model iteration process. Besides his industrial job, Yang also provides academic services to multiple HCI and Machine Learning venues.

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BibTeX
@conference {232941,
author = {Lezhi Li and Yunfeng Bai and Yang Wang},
title = {Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning at Uber},
year = {2019},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
month = may,
}