a combination of different factors:
- it is not thought in most self-educated programs.
- therefore most actually don't know that 1) it exists 2) how to do it 3) how to do power calculations
- since most don't know it, there is no demand for it
- costs compute time and resources, as well as human time, so it's skipped if nobody asks for it
- there is no standardized approach for ML models. Do you vary only the training, how to partition your dataset? there is no sklearn prebuilt stuff either
I find azure terrible for ml in general. They basically force you on databricks, azure ml studio just sucks compared to gcp vertex.
We're now on teradata for mlops, and it's surprisingly ok. High entry cost but overall a lot cheaper than what we used to have on azure/databricks, faster, and better.
We are forced on azure at work, but I used vertex for hobby projects