· MLOps · 2 min read
Monitoring Model Drift in Production: A Practical Guide
Models degrade over time. Learn strategies to detect data drift and concept drift before they impact your business ROI.
Deploying a model is not the finish line; it’s really the starting line. Unlike traditional software, which usually keeps working the same way until you change the code, Machine Learning models slowly get worse over time because the world around them changes. This is called Drift. A model trained on data from 2023 might be totally useless in 2025.
Understanding Drift
1. Data Drift
This happens when the data coming in looks different from the data we trained on.
- Example: An image scanner trained on sunny photos starts seeing cloudy photos.
- The Fix: We watch the statistics of the incoming data to spot changes.
2. Concept Drift
This is when the fundamental rules change.
- Example: Fraudsters work out how to trick our system, so a pattern that used to be safe is now malicious.
Watching the Watchmen
To keep our systems reliable, we need to keep a close eye on them:
Statistical Tests
We use maths to compare the new data against the old data:
- Kolmogorov-Smirnov (KS) Test: Good for checking numbers.
- Population Stability Index (PSI): A standard way banks check for shifts in data.
checking Proxies
Often, we don’t know the “Right Answer” straight away (e.g. did the customer pay back the loan?). We can’t wait months to find out if the model is broken. So we check proxies instead:
- Predictions: If the model suddenly starts flagging 50% of transactions as fraud instead of 1%, something is definitely wrong.
- Confidence: If the model suddenly becomes less sure of its answers, that’s a warning sign.
Automatic Fixes
When we spot drift, our MLOps platform generally kicks into gear:
- Alert: It sends a message to the Data Science team.
- Fallback: It might switch to a simpler, rules-based system or an older, safer model.
- Retrain: Ideally, it triggers a comprehensive re-training process using the newest data to learn the new patterns.
At Alps Agility, we build self-healing AI systems. We use tools like Arize AI and Evidently AI to give you peace of mind that your AI is still doing its job correctly.
Don’t fly blind. Make sure your models stay accurate and profitable. Speak to our AI reliability experts.
