The world we live in is constantly changing, and so is the data that is collected to build models. One of the problems that is often seen in production environments is that the deployed model doesn’t behave the same way as it did during the training phase. This concept is generally called data drift or dataset shift, and can be caused by many factors, such as bias in sampling data that affects features or label data, the non-stationary nature of time series data, or changes in the data pipeline. Because machine learning (ML) models aren’t deterministic, it’s …
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