AI Model Monitoring
Separate feature drift from measured prediction quality.
How it works
A deployed model may encounter data unlike its training set. Feature drift concerns changes in inputs; concept drift concerns a changing input/target relationship. A feature alert alone does not prove worse predictions, and stable inputs do not guarantee stable quality. This offline example compares two fixed fictional batches without running a monitoring service.
Run the code
Try in playground loads this complete standard-library Python example without executing it. Choose Run when ready. Local review uses Python 3.12.14; the existing browser editor uses Pyodide 0.27.7. These small fictional examples need no account, external data, model-provider key or database service.
Example
from statistics import mean
reference = [2,3,4]
current = [9,10,11]
shift = abs(mean(current)-mean(reference))
print(f"mean shift: {shift:.1f} trays")
print("review alert:", shift > 3)
print("current targets available:", False)
Expected result
mean shift: 7.0 trays review alert: True current targets available: False
The threshold is an author-chosen demonstration rule. Without observed current targets the example cannot calculate current accuracy. Operational work needs label delay handling, alert ownership, rollback criteria and a review of collection changes.