The following post is from Joseph Sirosh, Corporate Vice President of Machine Learning at Microsoft.
Maybe you haven’t noticed it, but machine learning – a way of applying historical data to a problem by creating a model and using it to successfully predict future behavior or trends – is 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. 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.
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 licenses can be expensive and popular programming languages for statistical computing have a steep learning curve. Even if a business could overcome these hurdles, deploying new machine learning models in production systems often requires months of engineering investment. Scaling, managing and monitoring these production systems requires the capabilities of a very sophisticated engineering organization, which few enterprises have today.
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. In mere hours, 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, which previews next month, 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. For customers, this means 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. 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.
Today, partners are using an early preview of Azure ML to build machine learning solutions for our customers. For example, MAX451 is helping a large retail customer determine what products a customer is most likely to purchase next, based on ecommerce data as well as brick and mortar store data. OSISoft is working with Carnegie Mellon University on real time fault detection and the diagnosis of energy output variations across campus buildings. Machine learning is helping to mitigate issues in real time and to predictively optimize energy usage and cost.
In July, we will release the Azure ML public preview and begin our journey to deliver machine learning with all the benefits of cloud computing for every organization. Azure ML is helping us realize that vision and, together with Microsoft’s data platform, customers will be able to create entirely new solutions that bring together big data insights, the Internet of things and predictive analytics. Visit our machine learning blog to learn more.