https://stacksimplify.com/tags/monitoring/Recent content in Monitoring on StackSimplify | DevOps & Cloud Education by Kalyan ReddyHugo -- gohugo.ioen-usTue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/data-drift-detection/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/data-drift-detection/Your model was trained on last year’s data. The world has moved on. Your model has not. Your model can return predictions with perfect latency, zero errors, 200 OK on every request. And every single prediction can be wrong. Operational monitoring tells you the model is running. Statistical monitoring tells you the model is still right. The Three Types of Drift Type What Changed Example Data Drift The inputs changed Model trained on ages 25-45, now seeing ages 18-22 Concept Drift The relationships changed High frequency used to mean fraud, now means power user Prediction Drift The outputs changed Fraud rate prediction jumped from 5% to 15% The DevOps Parallel Infrastructure monitoring: Is the server healthy?https://stacksimplify.com/blog/ml-model-monitoring/Tue, 14 Apr 2026 00:00:00 +0000https://stacksimplify.com/blog/ml-model-monitoring/Your ML model was 95% accurate when you deployed it. That was 6 months ago. Nobody has checked since. A model can show 10% CPU, zero errors, healthy pod status. And still return garbage predictions. Your Grafana dashboard shows all green. Your customers see wrong results. Why This Happens Your monitoring tracks CPU, memory, and pod restarts. Your model cares about none of that. Models degrade because the world changes: