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Review: Azure Machine Learning is for pros only

Microsoft’s machine learning cloud has the right stuff for data science experts, but not for noobs

Review: Azure Machine Learning is for pros only
Danqing Wang

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.

In the array of feature selection and solution algorithms available, Azure Machine Learning is similar to Databricks and IBM SPSS Modeler in giving you every tool you could possibly want. While that’s perfect for a data scientist, it’s a recipe for confusion for a business analyst. If you’re not a data scientist, but someone who, say, simply wants to predict next month’s sales so that the business can stock the right products, the Amazon Machine Learning approach of providing only one proven algorithm per class of problem may be better.

The learning process

Microsoft has a five-step introductory interactive tour of Azure Machine Learning that it will run for you at the drop of a hat. It’s impressive how quickly Azure Machine Learning can train a machine learning model from public demographic data and generate a Web service that will turn parameters into a prediction.

There is more than a little hand-waving going on here, however. Where did the model originate? How was it chosen? What data transforms needed to be applied? What are the residuals? How does it compare to other models? They don’t say.

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