This is the first post related to upcoming SQL Day conference at which I will have a chance to deliver a pre-conf “Data Scientist in the Cloud” (http://sqlday.pl/marcin-szeligamistrz-danych-data-scientist-w-chmurze/).
Microsoft Azure Machine Learning is a beautiful combination of two different approaches:
1. With growing library of predefined tasks you can easily perform complex action (like building a prediction model or transform your data) in one-click fashion.
2. With R script tasks you can execute in virtually way any R script you have.
Hence you have a easy to use (but still quite powerful) interface combined with almost infinite flexibility of R scripts.
But be careful — at least when this post was published (February 2015) you really can execute any R script, including modal functions, inside Azure ML Studio.
For example, a base function to load data is a read.table. R allows you to nest function (actually, the true power of R comes from nameless functions — they deserve separate post, for now you can think of them as a nested, declared on-the-fly functions). So, instead of using a fixed file name as a first parameter, we can feed read.table with file.choose function.
This function executed inside R Studio shows a familiar “Choose file” dialog box:
Anyone wants to guess what will be the result of executing this function inside Azure ML Studio?
Actually, in this case Azure ML Studio follows the original R concept of throwing at users as few massages as possible and the following script will run forever, without any warning:
See you at SQL Day!