Racks of servers housed inside the Microsoft data center in Dublin, Ireland. (Photo: Microsoft).

Microsoft Brings Machine Learning as a Service to Azure Cloud

Microsoft said it is launching a cloud-based machine learning service, called Azure ML, in July.

The service can be used to help create applications that can predict the future on the basis of previous data. It will help bake predictive analytics into applications, helping organizations use large amounts of data to provide all the benefits of machine learning to a wider audience.

Data analytics technology is increasingly used and looked at in general to help improve services. This kind of services was completely out of the realm of possibility pre-cloud, as the technology wasn’t there and the cost to power the infrastructure capable of doing it effectively was far too great for most companies. Bringing machine learning to cloud computing puts an extremely valuable tool within reach of thousands of developers.

“Microsoft Azure Machine Learning, a fully-managed cloud service for building predictive analytics solutions, helps overcome the challenges most businesses have in deploying and using machine learning. How? By delivering a comprehensive machine learning service that has all the benefits of the cloud,” Joseph Sirosh, corporate vice president of Machine Learning at Microsoft, wrote in a blog post.

Azure ML will bring together new analytics tools, powerful algorithms that Microsoft developed for products like Xbox and Bing, and the company’s many years of experience, into one easy-to-use cloud offering.  This knowledge, combined with the infrastructure of the Azure cloud, not only gives access to experience and years of learning, it eliminates the high-cost hurdle.

Azure ML will include a study tool for business analysts to get started, an API for deployment, and an SDK for writing applications. The open source R language will be used to write applications. Some select Microsoft partners are using an early version of the service, and the public preview will be available in July.

Data analysis is complex, requiring many skills, time and talent. Automation and platforms like Azure ML leverage Big Data, which many are talking about, but few are making full use of.

“The ease of implementation makes machine learning accessible to a larger number of investigators with various backgrounds–even non-data scientists,” said early adopter Bertrand Lasternas, of Carnegie Mellon University.

For those that already have large teams dedicated to this, it reduces their time and expense.

Azure ML will compete with the likes of IBM Watson. Google is also using Machine Learning in its own ways — one of which is to boost data center efficiency. Watson has enjoyed a number of applications, starting with destruction of regular folks like Ken Jennings on Jeopardy. IBM made it a cloud service, allowing companies to use structured and unstructured data to predict events like market opportunities and prevent fraud.

Azure ML offers a data science experience that is directly accessible to business analysts and domain experts, reducing complexity and broadening participation through better tooling,” noted early adopter Hans Kristiansen, of Capgemini.

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About the Author

Jason Verge is an Editor/Industry Analyst on the Data Center Knowledge team with a strong background in the data center and Web hosting industries. In the past he’s covered all things Internet Infrastructure, including cloud (IaaS, PaaS and SaaS), mass market hosting, managed hosting, enterprise IT spending trends and M&A. He writes about a range of topics at DCK, with an emphasis on cloud hosting.

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