AI model retraining is the process of updating a deployed machine learning model by feeding it new or more recent data to maintain or improve its performance. Over time, models can become stale or inaccurate due to shifts in data patterns, user behavior, market trends, or external conditions—a phenomenon known as model drift. Retraining helps correct for this drift by adapting the model to reflect the current environment, ensuring continued relevance and accuracy.
Retraining can be scheduled at regular intervals, triggered automatically based on performance degradation, or conducted manually as business needs change. It is a core practice within MLOps and AI lifecycle management, especially in high-impact applications such as fraud detection, predictive maintenance, customer segmentation, and personalization. Without proper retraining, organizations risk making flawed decisions based on outdated insights.