Modern Artificial Intelligence (AI) technologies, led by Machine Learning (ML), have gained unprecedented momentum over the past decade. Following this wave of ``AI summer", the network research community has also embraced AI/ML algorithms to address many problems related to network operations and management. However, compared to their counterparts in other domains, most ML-based solutions have yet to receive large-scale deployment due to insufficient maturity for production settings. This paper concentrates on the practical issues of developing and operating ML-based solutions in real networks. Specifically, we enumerate the key factors hindering the integration of AI/ML in real networks and review existing solutions to uncover the missing considerations. We also highlight two potential directions, i.e., MLOps and Causal ML, that can close the gap. We believe this paper spotlights the system-related considerations on implementing \& maintaining ML-based solutions and invigorate their full adoption in future networks.

翻译：以机器学习（ML）为代表的现代人工智能（AI）技术在过去十年间获得了前所未有的发展动力。紧随这波"AI热潮"，网络研究社区也积极采用AI/ML算法来解决网络运营管理中的诸多问题。然而，与其他领域的同类方案相比，大多数基于ML的解决方案由于在生产环境中成熟度不足，尚未实现大规模部署。本文聚焦于在真实网络中开发和部署ML解决方案的实践性问题。具体而言，我们逐一阐述了阻碍AI/ML融入真实网络的关键因素，并回顾现有方案以揭示被忽略的考量要素。我们还强调了两个有望弥合这一差距的潜在方向，即MLOps与因果机器学习。我们相信，本文聚焦于实施与维护ML解决方案时系统层面的考量，将为未来网络中全面采用这些方案注入新活力。