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　largescale　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　components.　Further,　we　highlight　a　promising　direction,　that　is,　machine　learning　operations　(MLOps),　that　can　close　the　gap.　We　believe　this　article　spotlights　the　system-related　considerations　on　implementing　and　maintaining　ML-based　solutions,　and　invigorates　their　full　adoption　in　future　networks.