Tags: #MachineLearning #MLOps #DataScience #ModelMonitoring #Python #AI Introduction Most organisations invest heavily in building and deploying machine learning models. They celebrate the launch, track accuracy at go-live, and move on. What they rarely account for is what happens next. The world changes. Customer behaviour shifts. Data distributions drift. And silently, without a single line of code changing, your model begins to fail. "A model that was 90% accurate at launch can degrade to the point of being worse than a coin flip — and most teams won't notice for months." This is the problem I set out to solve. The Hidden Cost of Model Drift In production ML, model drift is one of the most underestimated risks. A churn prediction model trained on last year's customer data may perform brilliantly at launch — but as market conditions evolve, as product offerings change, as customer demographics shift, the statistical patterns the model learned no longer reflect reality. The result? False confidence.…