Publication Type

Journal Article

Version

publishedVersion

Publication Date

9-2024

Abstract

We propose a framework for a cloud-based application of an image classification system that is highly accessible, maintains data confidentiality, and robust to incorrect training labels. The end-to-end system is implemented using Amazon Web Services (AWS), with a detailed guide provided for replication, enhancing the ways which researchers can collaborate with a community of users for mutual benefits. A front-end web application allows users across the world to securely log in, contribute labelled training images conveniently via a drag-and-drop approach, and use that same application to query an up-to-date model that has knowledge of images from the community of users. This resulting system demonstrates that theory can be effectively interlaced with practice, with various considerations addressed by our architecture. Users will have access to an image classification model that can be updated and automatically deployed within minutes, gaining benefits from and at the same time providing benefits to the community of users. At the same time, researchers, who will act as administrators, will be able to conveniently and securely engage a large number of users with their respective machine learning models and build up a labelled database over time, paying only variable costs that is proportional to utilization.

Keywords

Image classification, Cloud, AWS, Application, Crowd-sourced data

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Machine Learning with Applications

Volume

17

First Page

1

Last Page

25

ISSN

2666-8270

Identifier

10.1016/j.mlwa.2024.100583

Publisher

Elsevier BV

Copyright Owner and License

Publisher-CC-NC-ND

Additional URL

https://doi.org/10.1016/j.mlwa.2024.100583

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