论文标题
对网络AIOPS部署的深度学习模型的质量监控和评估
Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps
论文作者
论文摘要
人工智能（AI）最近引起了很多关注，从研究实验室过渡到许多领域的广泛部署，这对于深度学习（DL）技术尤其如此。最终，DL模型是软件工件，需要定期维护和更新：AIOPS是DevOps软件开发实践的逻辑扩展，以应用于网络操作和管理的AI软件。在DL模型部署的生命周期中，评估已部署模型的质量，检测“陈旧”模型并确定其更新的优先级很重要。在本文中，我们在网络管理的背景下介绍了这个问题，提出了（i）个人推理质量评估的简单而有效的技术，以及（ii）多种推论的总体模型质量跟踪，我们适用于两个用例，代表网络管理和图像识别领域。
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.
