Machine learning drift refers to the degradation of a model's performance over time, which can be caused by changes in user behavior, biased data, or inaccurate representations of actual data. There are two main types of model drift: concept drift, where input data hasn't changed but user behavior has, and data drift, where the properties of input or output data have changed. Detecting drift is crucial, and methods such as the Kolmogorov-Smirnov test and population stability index can be used to identify changes in the model's performance. To mitigate drift, companies should monitor their models, ensure consistent training and test data, retrain and redeploy models when necessary, implement data quality checks, and develop statistical metrics to track performance. By implementing these mechanisms, companies can curtail drift and improve the overall accuracy of their machine learning models.