In my previous post , we trained a churn prediction model and deployed it to a SageMaker endpoint. That's where most tutorials stop. But in production, deploying a model is only the beginning.

Models degrade. Customer behaviour shifts. Contract mix changes when competitors launch new offers. A model trained on January's data may be quietly wrong by June — and without active monitoring, nobody will know until the churn rate climbs and someone asks awkward questions in a board meeting.

This post covers the system I built to make that model self-maintaining : an end-to-end MLOps pipeline that retrains automatically, gates on model quality, and raises alerts the moment it detects something drifting.

What We're Building

EventBridge (daily schedule) │ ▼ Lambda: trigger_retraining │ ▼ SageMaker Pipeline ├── Phase 1: PreprocessData → encode, scale, split ├── Phase 2: TrainModel → SKLearn estimator ├── Phase 3: EvaluateModel → writes evaluation.json └── Phase 4: CheckAccuracy → ROC-AUC ≥ 0.80?…