<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Prometheus on StackSimplify | DevOps &amp; Cloud Education by Kalyan Reddy</title><link>https://stacksimplify.com/tags/prometheus/</link><description>Recent content in Prometheus on StackSimplify | DevOps &amp; Cloud Education by Kalyan Reddy</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Tue, 14 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://stacksimplify.com/tags/prometheus/index.xml" rel="self" type="application/rss+xml"/><item><title>ML Model Monitoring: Your Grafana Dashboard Is Lying to You</title><link>https://stacksimplify.com/blog/ml-model-monitoring/</link><pubDate>Tue, 14 Apr 2026 00:00:00 +0000</pubDate><guid>https://stacksimplify.com/blog/ml-model-monitoring/</guid><description>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:</description></item></channel></rss>