Why use Azure Functions for ML inference ?

This is interesting.  We just moved some scheduled IoT processing _off_ Azure functions for a few main reasons. 

1. The lack of any persistent storage (the functionality needed to regularly scrape through a couple of GB of data) 
2. The run-time limit of 10 minutes kept catching us out.  
3. The management/monitoring experience is pretty poor.

To be fair our use case was more like a cron job on a "VM-lite"  - i.e. we were looking for a cheap way to run a scheduled job every few hours with a moderate amount of persistent storage that wouldn't incur the full cost of a VM but that also seems to apply here.  It sounds like the persistency issue can now be resolved using Azure files but I'm curious about the run-time limitation - surely this is a factor in ML applications as well?