Impact of Resource Heterogeneity on MLOps Stages: A Computational Efficiency Study

Julio Corona, Pedro Rodrigues, Mário Antunes, Mário Antunes, Rui Aguiar, Rui Aguiar

2025

Abstract

The rapid evolution of hardware and the growing demand for Machine Learning (ML) workloads have driven the adoption of diverse accelerators, resulting in increasingly heterogeneous computing infrastructures. Efficient execution in such environments requires optimized scheduling and resource allocation strategies to mitigate inefficiencies such as resource underutilization, increased costs, and prolonged execution times. This study examines the computational demands of different stages in the Machine Learning Operations (MLOps) pipeline, focusing on the impact of varying hardware configurations characterized by differing numbers of Central Processing Unit (CPU) cores and Random Access Memory (RAM) capacities on the execution time of these stages. Our results show that the stage involving resource-intensive model tuning significantly influences overall pipeline execution time. In contrast, other stages can benefit from less resource-intensive hardware. The analysis highlights the importance of smart scheduling and placement, prioritizing resource allocation for model training and tuning stages, in order to minimize bottlenecks and enhance overall pipeline efficiency.

Paper Citation

in Harvard Style

Corona J., Rodrigues P., Antunes M. and Aguiar R. (2025). Impact of Resource Heterogeneity on MLOps Stages: A Computational Efficiency Study. In Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-757-3, SciTePress, pages 246-253. DOI: 10.5220/0013520600003964

in Bibtex Style

@conference{icsoft25,
author={Julio Corona and Pedro Rodrigues and Mário Antunes and Rui Aguiar},
title={Impact of Resource Heterogeneity on MLOps Stages: A Computational Efficiency Study},
booktitle={Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2025},
pages={246-253},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013520600003964},
isbn={978-989-758-757-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Impact of Resource Heterogeneity on MLOps Stages: A Computational Efficiency Study
SN - 978-989-758-757-3
AU - Corona J.
AU - Rodrigues P.
AU - Antunes M.
AU - Aguiar R.
PY - 2025
SP - 246
EP - 253
DO - 10.5220/0013520600003964
PB - SciTePress