Model drift, a decline in a model's predictive power due to changes over time, can hinder accurate decision-making in machine learning. It is essential to continuously monitor model performance and promptly retrain them with fresh data to adapt. Implementing strategies like MLOps and automation can track changes efficiently, and visual monitoring is helpful for detecting deviations. Highlighting the significance of diversified training datasets, the need for updates and retraining to mitigate the risk of drift is emphasized, ultimately leading to a more resilient and adaptable machine learning model, thus ensuring longevity and usefulness.