New Delhi, Nov. 4 -- Imagine training a dog to recognize different breeds. You show the dog pictures of various breeds, like Golden Retrievers and Labradors. Over time, new dog breeds are introduced, or the lighting conditions in the photos change. The dog, trained on old data, might struggle to recognize these new variations.

Similarly, data drift occurs in machine learning when the characteristics of the data used to train a model change over time. This can happen due to various reasons like changing trends, new data sources, or decreased data quality.

A machine learning model is trained on a specific dataset. Over time, the real-world data that the model encounters might change. This could be due to changes in trends, regulations, o...