Data Drift

Data drift refers to changes in the statistical properties or structure of input data over time that differ from the data used to train a model. This shift can degrade model performance by introducing new values, distributions, or categories that the model was not designed to handle. Monitoring for data drift is essential to ensure model accuracy, relevance, and compliance in production environments.

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