Relevance Debugging and Explaining at LinkedIn

Daniel Qiu and Yucheng Qian, LinkedIn

Abstract: 

At LinkedIn, we provide value to our members by serving information most relevant to them. Because of the complexity of the distributed system, it's hard for our AI engineers to understand how does the machine learning model make the prediction to provide the value to our members in the online system.

In this talk, we will cover how we provide the infrastructure to instrument our online relevance serving system to help AI engineers better understand their machine learning models and debug issues, and introduce two debugging tools that we provide for search and feed to visualize the relevance information.

Daniel Qiu, LinkedIn

Daniel Qiu is a software engineer at Linkedin. He has been working on infrastructure and tools for debugging the relevance machine learning model to improve the productivity of AI engineers and deliver Linkedin’s value to our members. Before that he was a computer science student at UCLA.

Yucheng Qian, LinkedIn

Yucheng Qian is a senior software engineer at LinkedIn working on improving productivity of AI engineers by providing effective debugging, explaining, and monitoring solutions. Previously, Yucheng has had years of experience developing both consumer-facing and professional-facing applications and led the effort to create the first machine learning platform at Jobcase Inc.

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BibTeX
@conference {232949,
author = {Daniel Qiu and Yucheng Qian},
title = {Relevance Debugging and Explaining at LinkedIn},
year = {2019},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
month = may,
}