Operationalizing AI in Future Networks: A Bird's Eye View from the System Perspective

Modern 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 article 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. Further, we highlight a promising direction, i.e., Machine Learning Operations (MLOps), 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.