Microsoft Azure ML -- big data modeling in Azure
Microsoft releases Azure ML (Machine Language), which provides data analytic tools in a Web-based environment.
Network World - Microsoft has jumped in with both feet with the release to Preview of a new Microsoft Azure-based tool that helps organizations do Machine Learning and predictive analysis all from a Web console.
I had an opportunity to work with Azure ML (codename "Passau") in an early adopter program, and we collaboratively built machine learning models WAY faster and easier than we've been able to do with traditional tools like SAS, SPSS, and R.
The tool allows for the import of data (or real time HTTP access to live data), and then traditional statistical analysis modeling, calculations, comparative analysis, forecasting, and the like. What made Azure ML a BETTER collaborative tool for us in our analysis work is the ability for us to share data, share models, and provide access to Web-views of our data models (through permissions) WITHOUT ever having to move the project data between users.
Normally we would create a model and then either cut/paste the algorithms and send them to one another, or save the data models and send the ENTIRE data model to others. In many instances, we don't necessarily want to share or give up the data model information to others, and in other instances we would run in to version control issues when data models were being edited and exchange across a workgroup.
With Azure ML, the data, the models, the analytics, EVERYTHING remains up in Microsoft Azure with shared access to experiments and models. AND we were able to control the models where we were able to setup Web views of the results of our models without EVER giving up access to the actual model analytics on the backend.
We were able to modify, revise, review, and change the models several times a day (and in some cases several times an hour), something that would have taken us days to do in the past.
So with this new tool, we set out to leverage the tool in a manner that would take what we were already doing the old fashion way, and put it into a production mode for a very high visibility real world scenario.
Two months later, here's the real live scenario we're using Azure ML for:
Scenario: "Using Microsoft Azure ML to Predict the Outcome of the 2014 U.S. Congressional Elections"
Background: More campaign money will be spent on the United States Congressional Elections in 2014 than has EVER been spent in history, and the ability to assess and predict the outcomes from the elections is of SIGNIFICANT importance. Every single seat in the U.S. House of Representatives is up for elections this Fall, and the Republican party wants to maintain its majority going into the 2016 Presidential elections. The Democratic party would very much want to retake the House and have a majority in both houses of Congress as it goes into the 2016 elections. If the Democrats control both houses of Congress, they have a good chance of seating a Democratic President in 2016. If the Republicans continue to control the House of Representatives and potentially gains seats in the House, they have a better chance of placing a Republican in the White House in 2016 and can undo a lot of what President Obama has put in place over the past 8-years.
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