What is Concept Drift in Applied AI?
The phenomenon where the underlying statistical relationship between input features and target outputs changes over time, even when input distributions appear stable. Distinguished from data drift (input distribution shift) and prediction drift (output distribution shift). Detection requires labeled outcomes from the production stream.
Why Concept Drift Matters for Your AI or Cybersecurity Career
Concept drift is the silent killer of long-running ML models. Engineers who monitor for concept drift with delayed-label feedback loops catch model degradation before customers do.
Related Applied AI Terms
The phenomenon where the underlying statistical relationship between input features and target outputs changes over time, even when input distributions appear stable. Distinguished from data drift (input distribution shift) and prediction drift (output distribution shift). Detection requires labeled outcomes from the production stream.
Concept drift is the silent killer of long-running ML models. Engineers who monitor for concept drift with delayed-label feedback loops catch model degradation before customers do.
Definitions are original explanations written for career development purposes. For authoritative technical definitions, refer to NIST, ISO, or the relevant standards body.
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