Model Drift

Model drift, the degradation of machine learning model performance over time due to changes in input data distribution, is a critical challenge hindering the reliable deployment of AI systems. Current research focuses on developing robust frameworks for detecting drift using techniques like Maximum Mean Discrepancy and addressing it through methods such as unsupervised domain adaptation, active learning, and data quality assessment, often applied to convolutional neural networks and other deep learning architectures. Understanding and mitigating model drift is crucial for ensuring the continued accuracy and reliability of AI in diverse applications, from medical diagnosis to industrial manufacturing and 5G network management. This necessitates ongoing research into both drift detection and effective adaptation strategies.