Model Monitoring
Definition
Model monitoring makes changes and problems in ongoing operation visible.
The continuous observation of performance, data, behavior, risks, and operation of a deployed model.
Simply explained
Like a control display, it shows whether a deployed model continues to work properly.
Why it matters in practice
Helps develop and operate digital systems robustly, securely, and maintainably.
Example from business
A dashboard tracks errors, drift, latency, and domain-specific thresholds with escalation.
Limits and pitfalls
- Only observing system metrics or using missing ground truth as an excuse for no control.
Origin & history
With productive ML systems, monitoring has been extended beyond technical availability to drift and impact.
Use in mid-sized companies
- Possible application: A dashboard tracks errors, drift, latency, and domain-specific thresholds with escalation.
Frequently asked questions
What does model monitoring mean?
The continuous observation of performance, data, behavior, risks, and operation of a deployed model.
Which mistakes should be avoided?
Only observing system metrics or using missing ground truth as an excuse for no control.
Sources
Source checked on 10.10.2026. Editorial use cases are hypothetical; no evidence of real customer results.
Editorially reviewed on: