AutoML: Towards automation of machine learning systems maintainability

0Citations
Citations of this article
9Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Machine learning systems both gained significant interest from the academic side and have seen adoption in the industry. However, one aspect that has received insufficient attention so far is the study of the lifecycle of such systems. This aspect is particularly important due to various ML systems' strong dependency on data, which is constantly evolving-and, therefore, changing-over time. The focus of my PhD research is the study of the implications of these dynamics on the ML systems' performance. Concretely, I propose a method of detecting changes caused by drift in the data early. Furthermore, I discuss possibilities for automating large parts of the ML lifecycle management, to ensure a better and more controllable maintenance process.

Cite

CITATION STYLE

APA

Poenaru-Olaru, L. (2021). AutoML: Towards automation of machine learning systems maintainability. In Middleware 2021 Doctoral Symposium - Proceedings of the 22nd International Middleware Conference: Doctoral Symposium (pp. 4–5). Association for Computing Machinery, Inc. https://doi.org/10.1145/3491087.3493674

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free