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


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


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.

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@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 = {},
publisher = {USENIX Association},
month = jul

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