Optimizing Machine Learning / Only process new data

[ThoSt81]

Not sure if this is the right place, but maybe one of you has solved this:

I have deployed a small setup through Azure AI Studio and everything is working fine.
As I add new data to my datastore, I need to update the index accordingly. To do this, I have scheduled the original job that was created in Machine Learning Studio (ml.azure.com). However, this appears to always process all the data that is in the data store and this will run for hours (22k html files, mostly <5kb per file).
I am adding about 50 files per day and would like to only add them (or updated files) to the index.
Is this possible? How?

The other question is that the training (serverless) is running for a very long time ~16 hours. Is this expected or can this be optimized (other than using a compute instance instead of serverless).

Thanks for your help/input!

[ThoSt81]

https://learn.microsoft.com/en-us/answers/questions/2146519/optimizing-machine-learning-only-process-new-data