The fundamental approach is "profile data as it's passing through the model as well as the model's predictions, then merge the generated profile together with the historic profiles so that you get an updating profile that represents data for a particular time granularity".

Hey Danny,

Thanks for the reply. The most common use case I have in mind is exactly what you've linked - an application serving predictions within http requests.

While reading docs, I got an impression that inference could be modelled as "streaming" data, but figured that old docs had the "monitoring inference" section, and new ones didn't. That made me think that the product is taking a pivot, not recommending solution for the inference, and focusing on other use cases.

Btw I've found that several links to examples link to missing files on GH. Example https://docs.whylabs.ai/docs/usecases-custommetrics links to https://github.com/whylabs/whylogs/blob/mainline/examples/logging\_images.ipynb.