Microsoft to provide drag-and-drop machine learning on Azure

Microsoft is stepping up its cloud computing game with a new service called Azure Machine Learning that users visually build and machine learning models, and then publish APIs to insert those models into applications. The service, which will be available for public preview in July, is one of the first of its kind and the latest demonstration of Microsoft’s heavy investment in machine learning.

Azure Machine Learning will include numerous prebuilt model types and packages, including recommendation engines, decision trees, R packages and even deep neural networks (aka deep learning models), explained Joseph Sirosh, corporate vice president at Microsoft. The data that the models train on and analyze can reside in Azure or locally, and users are charged based on the number of API calls to their models and the amount of computing resources consumed running them.

The reason why there are so few data scientists today, Sirosh theorized, is that they need to know so many software tools and so much math and computer science just to experiment and build models. Actually deploying those models into production, especially at scale, opens up a whole new set of engineering challenges. Sirosh said Microsoft hopes Azure Machine Learning will open up advanced machine learning to anyone who understands the R programming language or, really, anyone with a respectable understanding of statistics.

“It’s also very simple. My high school son can build machine learning models and publish APIs,” he said.

A screenshot of choosing a model in Azure Machine Learning.
A screenshot of choosing a model in Azure Machine Learning.

Of course, high schoolers probably won’t be the ones getting the most value out a service like this. And for companies with some machine learning know-how already, it’s likely the scalability and collaboration the cloud enables, as well as the API generation, that will be the real benefit. Early users OSIsoft and Carnegie Mellon University are using it to predict inefficiencies in buildings, Sirosh said, and a car manufacturer is analyzing sensor data from the assembly line to identify problems before they leave the factory.

Among large cloud providers, Microsoft is early to getting this type service into the market, but it seems unlikely that others won’t eventually follow suit. Google already offers services such as BigQuery and its Prediction API, and it seems logical it will try to expand its suite of data-analysis services even more into the machine learning space where it invests so many resources internally. Amazon Web Services has thus far been content to focus on storage, computing, databases and related infrastructure services, but one has to assume it’s ready to move into data analysis if there’s real demand there.

There are multiple startups also pushing focused machine learning and artificial intelligence capabilities over API, including AlchemyAPIBigML and Expect Labs. Some Hadoop companies, including cloud startup Mortar Data, are trying to make recommendation engines and other popular machine learning applications easier to build. Then, of course, there’s IBM, which is touting its Watson Developers Cloud for injecting cognitive computing into mobile and web apps, but which so far has limited access to the system.

Scott Guthrie, Microsoft’s executive vice president of cloud and enterprise computing, will join top executives from numerous cloud providers — including AWS, Google, IBM SoftLayer and Rackspace — at our Structure conference this week to discuss where the cloud market is heading and what types of services users can expect to see in the coming years.