Trustworthy Machine Learning Operations for Predictive Maintenance Solutions
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
With the ever-growing capabilities of data acquisition and computational units in industry, development, and deployment of data-driven models (e.g., predictive maintenance solutions) have become more abundant. However, when not trained and maintained properly, these models can be counterproductive as their predictions are not correct, reliable, or interpretable. In addition, unlike conventional software, the issues with such models manifest themselves in reduced productivity and not in forms of traceable software error. Therefore, in this proposal we aim to use model evaluation measures introduced in trustworthy AI operations (TrustAIOps) to trigger re-evaluation of different parts of the data pipeline and the deployed data-driven model given machine learning operations (MLOps) requirements. We argue that by creating an ecosystem capable of monitoring different aspects of a data-driven solution by integrating and managing the implementation concepts in TrustAIOps and MLOps, it is possible to boost the performance of models given the constant changes induced by the specifications of Industry 4.0.
How to Cite
##plugins.themes.bootstrap3.article.details##
Trustworthy AI, Machine learning operations, Industry 4.0, Predictive maintenance
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.