An Experimentation and Analytics Framework for Large-Scale AI Operations Platforms

Authors: 

Thomas Rausch, TU Wien; Waldemar Hummer and Vinod Muthusamy, IBM Research AI

Abstract: 

This paper presents a trace-driven experimentation and analytics framework that allows researchers and engineers to devise and evaluate operational strategies for large-scale AI workflow systems. Analytics data from a production-grade AI platform developed at IBM are used to build a comprehensive system and simulation model. Synthetic traces are made available for ad-hoc exploration as well as statistical analysis of experiments to test and examine pipeline scheduling, cluster resource allocation, or similar operational mechanisms.

OpML '20 Open Access Sponsored by NetApp

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BibTeX
@inproceedings {256630,
author = {Thomas Rausch and Waldermar Hummer and Vinod Muthusamy},
title = {An Experimentation and Analytics Framework for Large-Scale {AI} Operations Platforms},
booktitle = {2020 {USENIX} Conference on Operational Machine Learning (OpML 20)},
year = {2020},
url = {https://www.usenix.org/conference/opml20/presentation/rausch},
publisher = {{USENIX} Association},
month = jul,
}

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