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Hey @Aaron Pham! Great question - we work a lot with cases where the input features go through some level of transformation before they are ingested into the model, ex: one-hot encoding. Making sure that the data you are expecting in production matches the offline data schema/distribution requires a lot of data wrangling in online serving. One other unique case (not drift) that we hear about input features is about measuring data consistency between offline and online features. This is especially common with folks who have feature stores. We did a piece with Feast on how to monitor for data consistency to avoid training-serving skew: https://arize.com/blog/feast-and-arize-supercharge-feature-management-and-model-monitoring-for-mlops/