2022/02/28 by Lixuan Yang, Yang, Lixuan, Dario Rossi +1 · 1 citation
Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Software System Performance and Reliability
paper · pdf · doi:10.48550/arxiv.2202.13642
openalex publication_date 2022/02/28 · openalex created_date 2022/08/20 · openalex updated_date 2026/07/28
Artificial Intelligence (AI) has recently attracted a lot of attention, transitioning from research labs to a wide range of successful deployments in many fields, which is particularly true for Deep Learning (DL) techniques. Ultimately, DL models being software artifacts, they need to be regularly maintained and updated: AIOps is the logical extension of the DevOps software development practices to AI-software applied to network operation and management. In the lifecycle of a DL model deployment, it is important to assess the quality of deployed models, to detect "stale" models and prioritize their update. In this article, we cover the issue in the context of network management, proposing simple yet effective techniques for (i) quality assessment of individual inference, and for (ii) overall model quality tracking over multiple inferences, that we apply to two use cases, representative of the network management and image recognition fields.