REAL-TIME MACHINE LEARNING MODEL MONITORING: A FRAMEWORK FOR DRIFT DETECTION AND PERFORMANCE MANAGEMENT
Keywords:
Machine Learning Monitoring, Data Drift Detection, Real-time Performance Management, Distributed Computing Systems, ML Model ReliabilityAbstract
This technical article explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in monitoring production models, presenting a comprehensive framework for real-time drift detection and performance management. The article examines advanced monitoring architectures integrating continuous data analysis, automated alert systems, and sophisticated visualization tools to maintain model reliability and performance. Through article analysis of distributed computing implementations and dashboard design considerations, the article demonstrates how organizations can effectively detect and respond to model degradation, data drift, and performance issues. The framework encompasses multiple layers of monitoring, from system-level metrics to user experience optimization, while incorporating emerging technologies such as IoT and edge computing. The article investigates implementation strategies across various industries, presenting case studies validating comprehensive monitoring solutions' effectiveness. By addressing challenges in model maintenance, data drift detection, and performance optimization, this article provides a structured approach to building resilient ML monitoring systems that ensure sustained model performance and reliability in production environments.
References
Dimitrios Uzunidis, Panagiotis Karkazis, Helen C. Leligou, "Machine Learning Resource Optimization Enabled by Cross Layer Monitoring," in 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Manchester, UK, 2022, pp. 156-161. [Online]. Available: https://ieeexplore.ieee.org/document/9908055
Sudhi Sinha, Young M. Lee, "Challenges with developing and deploying AI models and applications in industrial systems," Discover Artificial Intelligence, vol. 1, no. 1, pp. 1-15, 2024. [Online]. Available: https://www.researchgate.net/publication/383198725_Challenges_with_developing_and_deploying_AI_models_and_applications_in_industrial_systems#:~:text=However%2C%20transitioning%20AI%20models%20from%20concept%20to%20full-scale,calling%20for%20a%20comprehensive%20approach%20to%20AI%20integration.
Y. Chen and R. Liu, "A Model Drift Detection and Adaptation Framework for 5G Networks," in 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), Athens, Greece, 2022, pp. 234-239. [Online]. Available: https://ieeexplore.ieee.org/document/9928667
S. Kumar, "An Ultimately Simple Concept Drift Detector for Data Streams," in 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia, 2021, pp. 567-572. [Online]. Available: https://ieeexplore.ieee.org/document/9659127
Haina Tang, Lian Duan, Jun Li, "A Performance Monitoring Architecture for IP Videoconferencing," in 2004 IEEE International Workshop on IP Operations and Management, Beijing, China, 2004, pp. 122-127. [Online]. Available: https://ieeexplore.ieee.org/document/1547591
George Fernandez I, J.Arokia Renjith, "An Approach on Performance Monitoring in Cloud Application," in 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, 2019, pp. 345-350. [Online]. Available: https://ieeexplore.ieee.org/document/8918800
Xiao-Xiang Ruan, Chao-Chin Wu, "Boost the Performance of Model Training with the Ray Framework for Large-Scale Machine Learning Applications," IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 4, pp. 878-889, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9971626
Xuemei Wang, Ping Wu, Siwei Lou, "Quality-Relevant Process Monitoring Based on Improved Concurrent Canonical Correlation Analysis," IEEE Transactions on Industrial Electronics, vol. 68, no. 9, pp. 8901-8910, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9455561
Dashboard Design Patterns, "Dashboard Design Patterns," in 2022 IEEE Visualization and Visual Analytics (VIS), Oklahoma City, OK, USA, 2022, pp. 167-172. [Online]. Available: https://dashboarddesignpatterns.github.io/
V. Ojanen; J. Koivuniemi, K. Blomqvist, "Strategic Competence Development and Monitoring in a Multi-Disciplinary Research Institute," in 2002 IEEE International Engineering Management Conference, Cambridge, UK, 2002, pp. 234-239. [Online]. Available:https://ieeexplore.ieee.org/document/1038489
IEEE Xplore "2014 IEEE International Conference on Software Maintenance and Evolution," [Online]. Available: https://ieeexplore.ieee.org/xpl/conhome/6969845/proceeding
Jaroslav Porubän, Milan Nosal, "Practical Experience with Task-Driven Case Studies," in 2014 IEEE International Conference on Industrial Engineering and Engineering Management, Bandar Sunway, Malaysia, 2014, pp. 234-239. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7107613
Othmane Friha, Mohamed Amine Ferrag et al., "Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies," IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 4, pp. 718-752, Apr. 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9374808
Ancuta-Pentronela Barzu, Mihai Barbulescu, "Horizontal Scalability towards Server Performance Improvement," in 2017 16th RoEduNet Conference: Networking in Education and Research (RoEduNet), Targu Mures, Romania, 2017, pp. 1-6. [Online]. Available: https://ieeexplore.ieee.org/document/8123729
