Creating Neural Networks using Azure Machine Learning Studio

Several weeks ago, Microsoft released a new tool and system to create machine learning models. I wrote an article titled “Creating Neural Networks using Azure Machine Learning Studio” in the September 2014 issue of Visual Studio Magazine. See


The system is Cloud-based (on Microsoft Azure). The backend, where computations are performed, is called Microsoft Azure Machine Learning (sometimes abbreviated MAML). The front-end UI part of the system is a Web application called Machine Learning Studio (ML Studio).

In the article, I describe, step by step, how to create a neural network model that predicts the species (either “setosa”, “versicolor”, or “virginica”) of an iris flower, based on four numeric features: sepal length, sepal width, petal length, and petal width. A sepal is a green leaf-like structure.

ML Studio is an almost completely drag and drop system. You drag items that represent either data or actions on data (functions or methods to a programmer) onto a design surface and then connect the modules.

Fig1- IrisExperiment

The graphical approach is much, much faster than creating a prediction model using code. On the downside, the SDK for the system has not yet been released so you can’t write custom modules, meaning you can only do whatever the built-in modules can do. An analogy is Lego. With a lot of Lego modules you can build a lot of cool things. But if you had some machine to design and create custom Lego pieces (like an SDK), you could build anything.

It will be interesting to see if Azure ML gains traction among developers, business analysts, and data scientists. I think Azure ML is very cool, but the technology landscape is littered with the carcasses of great technologies that never caught on because of bad marketing or bad timing or just bad luck.

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