MLOps at the edge in DDIL environments

Dinesh C. Verma; P. Santhanam

doi:10.1117/12.3013300

7 June 2024 MLOps at the edge in DDIL environments

Dinesh C. Verma, P. Santhanam

Proceedings Volume 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI; 130510V (2024) https://doi.org/10.1117/12.3013300
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Military operations invariably involve devices at the edge (e.g. sensors, drones, handsets of soldiers, etc.) In edge environments, good network connectivity cannot be assumed due to Denied, Degraded, Intermittent, or Low-bandwidth (DDIL) conditions. A DDIL environment poses unique challenges for deploying AI applications at the edge, particularly in the execution of Machine Learning Operations (MLOps). In this paper, we present a framework to address these challenges by considering three important dimensions: (i)the ML model lifecycle activities, (ii) specific DDIL induced challenges at the edge and (iii) the application stack. We discuss three realistic use cases in detail to explain the use of this approach to identify the underlying design patterns. We believe that use of this framework can lead to a responsive and reliable AI deployment under varying operational conditions.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dinesh C. Verma and P. Santhanam "MLOps at the edge in DDIL environments", Proc. SPIE 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 130510V (7 June 2024); https://doi.org/10.1117/12.3013300

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