Detect Data & Model Drift
Catch when the world changes under you.
Detect Data & Model Drift is a free Deep Learning Academy lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Deep Learning Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
The World Keeps Moving
Your model learned yesterday's patterns, but real data keeps changing. When it shifts, predictions quietly get worse. We call this problem drift. 🌊
Two Flavors of Drift
There are two kinds to watch. Data drift means the inputs change shape, while concept drift means the relationship between inputs and labels changes.
A Concrete Example
A spam filter trained on old emails sees new tricks it never met. The inputs drifted, so its once-sharp accuracy slowly decays in production.
Watch the Input Distribution
The first signal of drift is in the features. Compare the distribution of recent inputs to your training data and look for a shift in the shape.
ref_mean = X_train.mean(axis=0)
live_mean = X_live.mean(axis=0)Measure the Gap
To quantify how far two distributions diverge, use a statistic like KS, the Kolmogorov-Smirnov test, which returns a number plus a p-value.
from scipy.stats import ks_2samp
stat, p = ks_2samp(X_train[:,0], X_live[:,0])Set a Threshold
A small p-value means the live data no longer matches training. Pick a threshold and trigger an alert whenever the gap crosses it.
if p < 0.05:
print("drift detected")Watch Predictions Too
Even without labels, the spread of your model's outputs is a clue. A sudden shift in predicted probabilities often signals incoming drift.
The Best Signal Is Truth
When real labels eventually arrive, compare them to past predictions. Falling live accuracy is the clearest, most direct proof that drift is hurting you.
Tools That Watch for You
You need not build everything by hand. Libraries like Evidently compute drift reports across all features and flag the columns that moved.
from evidently.report import ReportMonitor Continuously
Drift is not a one-time check. Run these comparisons on a schedule so your monitoring catches slow shifts long before users complain.
Detection Is Half the Battle
Spotting drift early lets you act before damage spreads. The natural next move is to retrain on fresh data and restore the model's edge.
Quick Check
What does concept drift specifically describe?
Recap
You learned to spot drift: compare live inputs to training, measure the gap with tests, watch predictions, and monitor on a schedule. 🎉
Frequently asked questions
Is the “Detect Data & Model Drift” lesson free?
Yes — the full text of “Detect Data & Model Drift” is free to read here on the web, and the Deep Learning Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Deep Learning Academy course, upgrade to CoddyKit PRO.
What will I learn in “Detect Data & Model Drift”?
Catch when the world changes under you. You practise Deep Learning Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Deep Learning Academy?
No prior experience is required. Deep Learning Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Detect Data & Model Drift” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Deep Learning Academy lesson?
Yes. Every Deep Learning Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Track Experiments with Weights & Biases
- Version Data & Models
- Detect Data & Model Drift
- Automate Retraining Pipelines