Microsoft Introduces Machine Learning For The Azure Cloud

Microsoft will be adding a Machine Learning (ML) component to its Azure cloud service in order to improve the accuracy of predictive analytics.

Azure ML brings together the tools developed internally for projects like Xbox and Bing, and builds on the work carried out by Microsoft Research. It is currently being tested by select partners, and the company hopes to publicly launch the platform in July.

“Soon, machine learning will help to drastically reduce wait times in emergency rooms, predict disease outbreaks and predict and prevent crime. To realize that future, we need to make machine learning more accessible – to every enterprise and, over time, everyone,” wrote Joseph Sirosh, corporate VP of Machine Learning, on the Microsoft blog.

IBM is making moves in a similar direction: the company recently launched a new business unit which aims to commercialise the artificial intelligence technology developed for the Watson project.

Brain in the cloud

Machine learning is already used by large corporations to optimise their services, detect fraud and predict customer demand. But according to Sirosh, complexity of deployment is preventing these valuable tools from finding wider adoption, especially among smaller businesses.

“Machine learning today is usually self-managed and on premises, requiring the training and expertise of data scientists. However, data scientists are in short supply, commercial software licences can be expensive and popular programming languages for statistical computing have a steep learning curve,” he wrote.

“Even if a business could overcome these hurdles, deploying new machine learning models in production systems often requires months of engineering investment.”

Microsoft hopes to accelerate the development and adoption of machine learning technologies by offering them as a fully-managed cloud service that could enable SMBs to predict future trends based on historic data.

According to the feedback from Microsoft’s partners who are already testing the service, its interface is so simple it can be used by your average IT staff, not just data scientists.

Sirosh adds that the service can be deployed in hours instead of weeks or months, and will be considerably cheaper than an in-house machine learning project.

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