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All Data Drift is Not Created Equal...
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Predictive models learn patterns in training data and use that information to predict target values for new data. There are two data sets at play in this process, the training data and the scoring or inference data. The model will work well in production (i.e., produce accurate predictions in line with expectations) when the new inference data is similar to the training data. When these two data sets are different, however, the model can become less accurate and produce unexpected results. Luckily, this is something that usually happens over time, so you have a chance to catch these issues if you are paying attention.
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