Feature drift monitoring as a service for machine learning models at scale


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In this talk, you’ll learn about techniques used to build a feature drift detection as a service capability for your enterprise and beyond. Feature drift monitoring is a way to check volatility of machine learning model inputs. It can trigger investigations for potential model degradation as well as explain why models have shifted.


Keira Zhou

Keira is a data engineering manager at Capital One who has hands-on expertise in both Data Engineering and Data Science. She has built streaming and batch data pipelines, deployed machine learning models in production, and is currently focusing on feature monitoring. Prior to Capital One, Keira has received her master degree focusing on Nature Language Processing.

Noriaki Tatsumi

A technology leader with expertise in MLOps, data engineering, and performance engineering. Currently focusing on advancing ML and Data Enablement at Capital One’s Card Technology Division. Prior to joining Capital One, served as the Director of Engineering at Blackboard as well as a co-founder of startups, Anax Security and Finalist Corporation.