David Crook shows how to build a model using Python’s SciKit library and then operationalize it in Azure ML:
Why Model Outside Azure ML?
Sometimes you run into things like various limitations, speed, data size or perhaps you just iterate better on your own workstation. I find myself significantly faster on my workstation or in a jupyter notebook that lives on a big ol’ server doing my experiments. Modelling outside Azure ML allows me to use the full capabilities of whatever infrastructure and framework I want for training.
So Why Operationalize with Azure ML?
AzureML has several benefits such as auto-scale, token generation, high speed python execution modules, api versioning, sharing, tight PaaS integration with things like Stream Analytics among many other things. This really does make life easier for me. Sure I can deploy a flask app via docker somewhere, but then, I need to worry about things like load balancing, and then security and I really just don’t want to do that. I want to build a model, deploy it, and move to the next one. My value is A.I. not web management, so the more time I spend delivering my value, the more impactful I can be.
Read the whole thing.