  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 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.
