Benefits of Layered Software Architecture in Machine Learning Applications

Ármin Romhányi, Zoltán Vámossy

2021

Abstract

The benefits of layering in software applications are well-known not only to authors and industry experts, but to software enthusiasts as well because the layering provides a testable and more error-proof framing for applications. Despite the benefits, however, the increasingly popular area of machine learning is yet to embrace the advantages of such a design. In the present paper, we aim to investigate if characteristic benefits of layered architecture can be applied to machine learning by designing and building a system that uses a layered machine learning approach. Then, the implemented system is compared to other already existing implementations in the literature targeting the field of facial recognition. Although we chose this field as our example for its literature being rich in both theoretical foundations and practical implementations, the principles and practices outlined by the present work are also applicable in a more general sense.

Paper Citation

in Harvard Style

Romhányi Á. and Vámossy Z. (2021). Benefits of Layered Software Architecture in Machine Learning Applications. In Proceedings of the International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-511-1, pages 66-72. DOI: 10.5220/0010424500660072

in Bibtex Style

@conference{improve21,
author={Ármin Romhányi and Zoltán Vámossy},
title={Benefits of Layered Software Architecture in Machine Learning Applications},
booktitle={Proceedings of the International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2021},
pages={66-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010424500660072},
isbn={978-989-758-511-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Benefits of Layered Software Architecture in Machine Learning Applications
SN - 978-989-758-511-1
AU - Romhányi Á.
AU - Vámossy Z.
PY - 2021
SP - 66
EP - 72
DO - 10.5220/0010424500660072