Category: Big Data & Data Science

Great! You’ve created a promising machine learning model using AzureML Studio. How do you make it available to colleagues for practical use? In Microsoft-speak, how do you “operationalize” it? The AzureML Studio makes it very easy to convert an existing experiment into an Azure web service that can be accessed from any application that supports […]

I, like many others, am constantly frustrated by the sensationalist use of terminology in data science. Twitter user @xaprb neatly summed up my own feelings when he tweeted When you’re fundraising, it’s AI [Artificial Intelligence]. When you’re hiring, it’s ML [Machine Learning]. When you’re implementing, it’s linear regression. When you’re debugging, it’s printf(). As someone […]

Anyone who has ever done a linear regression in R has seen an R formula. R formulae are examples of the Wilkinson notation, sometimes called the Wilkinson-Rogers notation. This same notation is used by other applications including Matlab and Octave to indicate relationships between variables. In most cases, R users have seen the Wilkinson notation […]

Choosing a machine learning (ML) platform can be a cause of considerable stress. It’s a major commitment you will have to live with for years and an unwise decision might even affect your job prospects. Fortunately, asking yourself a few fundamental questions can simply the decision considerably. Are you primarily a Windows Shop? In recent […]

Microsoft recently released a preview of a machine learning framework for .NET developers—ML.NET. I needed to perform a clustering analysis from existing data in one of my applications. This is a pretty common machine learning task, so I decided to document the basic approach in this article. We’ll use the well-worn iris data set from […]