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Microsoft unveils Azure ML: machine learning for Cloud

Brian Karlovsky | June 18, 2014
Microsoft has paved the way for machine learning to drastically reduce wait times in emergency rooms, predict disease outbreaks and even predict crime with the launch Azure ML.

Microsoft has paved the way for machine learning to drastically reduce wait times in emergency rooms, predict disease outbreaks and even predict crime with the launch Azure ML.

The software giant is set to launch a Cloud service which uses machine learning and predictive analytics to help customers build data-driven applications to predict, forecast and change future outcomes.

Azure ML, which will preview in July, is a fully managed cloud service for building predictive analytics solutions combining the power of a comprehensive machine learning service with all the benefits of cloud.

Azure ML will be available for US only datacentres at preview in July.

Microsoft corporate vice president of machine learning, Joseph Sirosh, said machine learning - a way of applying historical data to a problem by creating a model and using it to successfully predict future behaviour or trends — was touching more and more lives every day.

"For example, search engines, online product recommendations, credit card fraud prevention systems, GPS traffic directions and mobile phone personal assistants like Cortana all use the power of machine learning," he said.

"But we've barely scratched the surface of its potential to change the world.

"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, every one.

However, Sirosh said machine learning was usually self-managed and on-premises, requiring the training and expertise of data scientists - which are in short supply.

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

"Scaling, managing and monitoring these production systems requires the capabilities of a very sophisticated engineering organization, which few enterprises have today."

With Azure ML, customers and partners can build data-driven applications to predict, forecast and change future outcomes — a process that previously took weeks and months.

Azure ML, will bring together the capabilities of new analytics tools, powerful algorithms developed for Microsoft products like Xbox and Bing, and years of machine learning experience into one simple and easy-to-use cloud service, according to a company statement

Sirosh said this meant virtually none of the startup costs associated with authoring, developing and scaling machine learning solutions.

"Visual workflows and startup templates will make common machine learning tasks simple and easy," he said. "And the ability to publish APIs and Web services in minutes and collaborate with others will quickly turn analytic assets into enterprise-grade production cloud services.

Examples of use cases using machine learning include, MAX451, which is helping a large retail customers determine what products a customer is most likely to purchase next, based on e-commerce data as well as brick and mortar store data.

 

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