Eric Mamet (4/25/2015)
I just saw a (new?) book entitled "Mastering SQL Server 2014 Data Mining"...Following Microsoft BI developments, I was under the impression that "on Premise" SQL Server data mining was a dying bread and that it was simply getting replaced by Machine Learning (cloud based)...
Did I get that wrong? :doze:
Is there still a point starting using SQL Server (on premise) data mining, or should we skip this and head straight for the clouds?
Thanks
Eric
I don't know what the general opinion is of the masses, but I can tell you my perspective.
Our company does machine learning in digital advertising. We use a lot of R and a little bit of Python to accomplish these tasks. Our data all lives in the Microsoft stack however.
Most of our guys use R, Python and SAS. Very few are familiar with Microsoft Data Mining with SSAS. This is because not a lot of people are learning how to do machine learning on the Microsoft stack. They are learning basic SQL and mostly how to use NoSQL solutions or cloud based solutions.
That said, Microsoft Data Mining in our camp could be very useful. Our data lives in the Microsoft stack and our data mining packages can do predictive analytics in the same location where the data lives. It's just the sheer fact that our data scientist guys are not as familiar with SSAS nor can get passed the steep learning curve in using it versus R, Python and SAS that are insanely more familiar and flexible on the fly.
Outside of that, it's hard to compete with cloud solutions like Microsoft Azure ML, Amazon Redshift and Google Compute/BigQuery that can have you computing data across dozens of machines without impacting your stack.
Azure ML specifically combines SSIS and Machine Learning with Python and R very nicely. It's a great option than using Microsoft Data Mining that doesn't seem to be going anywhere.