1. Background
In many real-world applications where machine learning models have been deployed in production, often the data evolve over time and thus models built for analyzing such data quickly become obsolete over time. It becomes essential for data scientists to monitor the model performance over time. Is the machine learning model deployed sustainable and performing consistently? Usually, the scenario that occurs over time, is not because the model stops performing well but simply because the model can no longer capture the right variability of the data be it the dependent or independent variables. The reason for this
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