Finding Bottleneck in Machine Learning Model Life Cycle

Authors: 

Chandra Mohan Meena, Sarwesh Suman, and Vijay Agneeswaran, WalmartLabs

Abstract: 

Our data scientists are adept in using machine learning algorithms and building model out of it, and they are at ease with their local machine to do them. But, when it comes to building the same model from the platform, they find it slightly challenging and need assistance from the platform team. Based on the survey results, the major challenge was platform complexity, but it is hard to deduce actionable items or accurate details to make the system simple. The complexity feedback was very generic, so we decided to break it down into two logical challenges: Education & Training and Simplicity-of-Platform. We have developed a system to find these two challenges in our platform, which we call an Analyzer. In this paper, we explain how it was built and it’s impact on the evolution of our machine learning platform. Our work aims to address these challenges and provide guidelines on how to empower machine learning platform team to know the data scientist’s bottleneck in building model.

OpML '20 Open Access Sponsored by NetApp

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BibTeX
@inproceedings {256632,
author = {Chandra Mohan Meena and Sarwesh Suman and Vijay Agneeswaran},
title = {Finding Bottleneck in Machine Learning Model Life Cycle},
booktitle = {2020 {USENIX} Conference on Operational Machine Learning (OpML 20)},
year = {2020},
url = {https://www.usenix.org/conference/opml20/presentation/meena},
publisher = {{USENIX} Association},
month = jul,
}

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