A Model-Driven Engineering Approach for Monitoring Machine Learning Models

TY - GEN

T1 - A Model-Driven Engineering Approach for Monitoring Machine Learning Models

AU - Kourouklidis, Panagiotis

AU - Kolovos, Dimitris

AU - Noppen, Joost

AU - Matragkas, Nicholas

N1 - Funding Information: ACKNOWLEDGMENT The work in this paper has been partially supported by the Lowcomote project, that received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curiegrant agreement No 813884. Publisher Copyright: © 2021 IEEE.

PY - 2021/12/20

Y1 - 2021/12/20

N2 - Once a machine learning (ML) model is produced and used for commercial purposes, it is desirable to continuously monitor it for any potential performance degradation. Domain experts in the area of ML, commonly lack the required expertise in the area of software engineering, needed to implement a robust and scalable monitoring solution. This paper presents an approach based on model-driven engineering (MDE) principles, for detecting and responding to events that can affect a ML model's performance. The proposed solution allows ML experts to schedule the execution of drift detecting algorithms on a computing cluster and receive email notifications of the outcome without requiring extensive software engineering knowledge.

AB - Once a machine learning (ML) model is produced and used for commercial purposes, it is desirable to continuously monitor it for any potential performance degradation. Domain experts in the area of ML, commonly lack the required expertise in the area of software engineering, needed to implement a robust and scalable monitoring solution. This paper presents an approach based on model-driven engineering (MDE) principles, for detecting and responding to events that can affect a ML model's performance. The proposed solution allows ML experts to schedule the execution of drift detecting algorithms on a computing cluster and receive email notifications of the outcome without requiring extensive software engineering knowledge.

KW - Concept Drift

KW - Data Drift

KW - Dataset Shift

KW - Machine Learning

KW - Model-Driven Engineering

UR - http://www.scopus.com/inward/record.url?scp=85124011155&partnerID=8YFLogxK

U2 - 10.1109/MODELS-C53483.2021.00028

DO - 10.1109/MODELS-C53483.2021.00028

M3 - Conference contribution

AN - SCOPUS:85124011155

T3 - Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021

SP - 160

EP - 164

BT - Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021

Y2 - 10 October 2021 through 15 October 2021

ER -