Microsoft’s PR department has been hard at work over the past week and all of the news has dealt with enhancements to Microsoft’s Azure cloud offering. The latest announcement is in relation to Microsoft’s Azure Machine Learning Preview.
The Azure Machine Learning Preview will allow end users to setup complex machine learning tasks. This enables Azure to learn from data thus allowing the machine learning service to create statistical models which can be used to create artificial intelligence tasks.
On a TechNet blog, Microsoft discusses the barriers of traditional machine learning. Microsoft writes, “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.”
Microsoft presents their solution to this barrier by saying, “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.
What about the future of Azure Machine Learning? The TechNet blog article states, “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.”
Joseph Sirosh, VP of Machine Learning at Microsoft, envisions a future where enterprises use machine learning as a way to harness data and make it useful within an organization. Sirosh writes, “To realize that future, we need to make machine learning more accessible – to every enterprise and, over time, every one.”