Originally published at mlopslab.org — updated weekly. 0 sponsors, 0 affiliate links.

⚡ The problem in one sentence: Your model shipped and worked great on day one. Now, weeks later, it's making worse decisions — silently, without throwing a single error. Drift detection is how you catch this before the damage is done.

Table of Contents

What is model drift detection?

Three types of drift you must monitor

Why it matters in production

Statistical methods for drift detection

Tools comparison

Step-by-step tutorial with Evidently AI

When drift is detected — what to do

FAQ

1. What is model drift detection?

Model drift detection is the practice of monitoring ML models in production to identify when they start degrading due to changes in real-world data.

Without it, a model that worked perfectly at deployment starts making worse predictions — often silently, without any errors or alerts. This is the #1 reason ML projects fail in production.…