Abstract
The article presents the design and deployment of a production-grade MLOps infrastructure that integrates edge computing with cloud-based resources for efficient machine learning operations. The system connects vehicle-mounted sensors (cameras and LiDAR) with a centralized data lake and a Kubernetes-based cloud environment. A fully automated pipeline, developed using Apache NiFi, manages continuous data acquisition, preprocessing, metadata registration, and model lifecycle orchestration. Data collected on edge devices are stored on a shared NAS and seamlessly transferred to the cloud for large-scale training and evaluation. The system allows for safe access, flexibility, and processing based on metadata, making it easy to scale and repeat machine learning workflows that meet current MLOps standards. The system has been validated in real-world deployments, confirming its applicability to safety-critical scenarios.
Paper Type
Poster
DOI
10.62036/ISD.2025.5
Design and Deployment of an Edge-Aware MLOps System for Multimodal Sensor Data
The article presents the design and deployment of a production-grade MLOps infrastructure that integrates edge computing with cloud-based resources for efficient machine learning operations. The system connects vehicle-mounted sensors (cameras and LiDAR) with a centralized data lake and a Kubernetes-based cloud environment. A fully automated pipeline, developed using Apache NiFi, manages continuous data acquisition, preprocessing, metadata registration, and model lifecycle orchestration. Data collected on edge devices are stored on a shared NAS and seamlessly transferred to the cloud for large-scale training and evaluation. The system allows for safe access, flexibility, and processing based on metadata, making it easy to scale and repeat machine learning workflows that meet current MLOps standards. The system has been validated in real-world deployments, confirming its applicability to safety-critical scenarios.
Recommended Citation
Karbowiak, L., Piatkowski, J. & Kuczynski, L. (2025). Design and Deployment of an Edge-Aware MLOps System for Multimodal Sensor DataIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.5