Concept Drift
The short answer · 21 words
In short
Change in the relationship between input data and target variable, which can make a previously suitable model less reliable over time.
Flow-alerta.cab (2026): Concept Drift. https://flow-alerta.cab/en/glossar/concept-drift. Editorially reviewed on: 09 October 2026.
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Practical
Relevant for productive ML systems, because business processes, customers, prices or behavior do not remain permanently stable.
Hypothetical example: A termination model loses accuracy after a new contract model fundamentally changes customer behavior.
Regularly monitor performance metrics and data distributions, check causes and trigger retraining or model changes only if there is a traceable need.
At a glance
- Core
- Concept drift changes the underlying pattern that a model has learned. The same entrance may later have a different meaning for the destination. This differs from pure data drift, in which only the distribution of inputs is shifted.
- Use
- Relevant for productive ML systems, because business processes, customers, prices or behavior do not remain permanently stable.
- Example
- Hypothetical example: A termination model loses accuracy after a new contract model fundamentally changes customer behavior.
- Limits
- Equate concept drift and data drift; monitor only input distributions; do not check model performance with new labels; retrain automatically with each drift.
In context
Questions about this term
Is Concept Drift the same as Data Drift?
No.
Can drift occur seasonally?
Yes.
Does every drift signal have to lead to retraining?
No.
All details and background
Concept drift changes the underlying pattern that a model has learned. The same entrance may later have a different meaning for the destination. This differs from pure data drift, in which only the distribution of inputs is shifted.
ELI5 – Explained simply
The world is changing, but the model still works according to old contexts.
Why it matters in practice
Relevant for productive ML systems, because business processes, customers, prices or behavior do not remain permanently stable.
Example
Hypothetical example: A termination model loses accuracy after a new contract model fundamentally changes customer behavior.
Pitfalls and limits
- Equate concept drift and data drift; monitor only input distributions; do not check model performance with new labels; retrain automatically with each drift.
Origin & history
Concept drift is a monitoring theme for productive models: the relationship between input and goal can change after training.
Use in mid-sized companies
- Regularly monitor performance metrics and data distributions, check causes and trigger retraining or model changes only if there is a traceable need.
Related terms
- Data drift
- Change point detection
- MLOs
- Model monitoring
Matching Flow-alerta service
AI opportunity analysis & roadmapAI consulting: AI potential analysis & roadmap on tasks, data and prerequisites of a possible first AI use case.Matching tool / use case
View use casesUse Case: Monitoring a forecasting model for changing customer or process contexts.Sources
- www.ibm.comIBM, What Is Model Drift?
Quellenprüfung: 09.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.
Editorially reviewed on: