Concept drift refers to a phenomenon where the statistical properties of target data change over time, leading to a degradation in performance and potential system failures in edge AI systems. It occurs due to noise and environmental changes, creating a gap between the trained dataset and the deployed environment. Concept drift detection and retraining of neural network models are necessary to address this gap.