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

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