Challenges and Experiences with MLOps for Performance Diagnostics in Hybrid-Cloud Enterprise Software Deployments

Authors: 

Amitabha Banerjee, Chien-Chia Chen, Chien-Chun Hung, Xiaobo Huang, Yifan Wang, and Razvan Chevesaran, VMware Inc

Abstract: 

This paper presents how VMware addressed the following challenges in operationalizing our ML-based performance diagnostics solution in enterprise hybrid-cloud environments: data governance, model serving and deployment, dealing with system performance drifts, selecting model features, centralized model training pipeline, setting the appropriate alarm threshold, and explainability. We also share the lessons and experiences we learned over the past four years in deploying ML operations at scale for enterprise customers.

OpML '20 Open Access Sponsored by NetApp

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BibTeX
@inproceedings {256640,
author = {Amitabha Banerjee and Chien-Chia Chen and Chien-Chun Hung and Xiaobo Huang and Yifan Wang and Razvan Chevesaran},
title = {Challenges and Experiences with MLOps for Performance Diagnostics in Hybrid-Cloud Enterprise Software Deployments},
booktitle = {2020 {USENIX} Conference on Operational Machine Learning (OpML 20)},
year = {2020},
url = {https://www.usenix.org/conference/opml20/presentation/banerjee},
publisher = {{USENIX} Association},
month = jul,
}

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