MLOps

MLOps is what keeps a machine-learning model useful after the data scientists have moved on. Training a model is the easy, glamorous part; the hard part is everything around it — versioning data and models, deploying reliably, monitoring for drift, and retraining on a schedule or trigger. In a telecom setting that lifecycle discipline matters more than usual, because the thing being modelled (traffic, mobility, interference) is constantly changing.

That last point is the crux: model drift. A model trained on last quarter's traffic quietly degrades as user behaviour, the network topology, and even the seasons shift, and without monitoring you won't notice until decisions start going wrong. So a serious MLOps pipeline watches live performance, flags drift, and either retrains automatically or alerts an engineer. As operators push ML into the core (NWDAF) and into operations (AIOps), MLOps is the unglamorous plumbing that decides whether those models stay trustworthy in production or rot.