Machine learning is an obvious complement to a cloud service that also handles big data. Often the major reason to collect massive amounts of observables is to predict other values of interest to the business. For example, one of the reasons to collect massive numbers of anonymized credit card transactions is to predict whether a new transaction is valid or fraudulent with some likelihood.
It’s no surprise then that Microsoft, with a large AI research department, would add machine learning facilities to its Azure cloud. Perhaps because the technology originated with the researchers, the commercial offering has all of the complex models and algorithms that a statistics and data weenie could want. In addition, Azure Machine Learning (a part of the Cortana Analytics Suite) has reduced model training and evaluation pipeline design to a drag-and-drop exercise, while also allowing users to add their own Python or R modules to the data pipeline.
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