eWEEK content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.
Next month, Microsoft will kick off a preview of Azure Machine Learning (Azure ML), a new cloud-based service aimed at helping businesses jump-start their predictive analytics initiatives.
A Microsoft spokesperson commented in an email to eWEEK that the upcoming offering “will allow developers and data scientists to build advanced analytic cloud services in minutes and hours, eliminating much of the heavy lifting associated with deploying machine learning in modern data-driven applications.” Azure ML will provide customers with “the fastest, easiest, and cheapest way to manage and deploy machine learning from the Azure portal,” said the company representative.
Microsoft is working on “a fully managed cloud service for building predictive analytics solutions,” wrote Joseph Sirosh, corporate vice president of machine learning at Microsoft, in a June 16 blog post. By toiling for just a few hours, customers and partners “can build data-driven applications to predict, forecast and change future outcomes—a process that previously took weeks and months,” he claimed.
Azure ML is Microsoft’s solution for an industry-wide problem that’s keeping organizations from capitalizing on advanced business analytics. A big data skills shortage, combined with the cost of implementing and maintaining a big data IT environment, in both time and money, makes it a non-starter for many businesses.
Sirosh noted that data scientists are in “short supply”—good news for job seekers, but bad news for enterprises. Salaries in the $300,000-per-year range are common as businesses scramble to get them on the payroll.
Lofty pay rates aside, businesses must also surmount other big IT hurdles. Sirosh added that “commercial software licenses can be expensive, and popular programming languages for statistical computing have a steep learning curve.”
In short, only well-funded, tech-savvy businesses need apply. “Scaling, managing and monitoring these production systems requires the capabilities of a very sophisticated engineering organization,” said Sirosh. Microsoft, as it turns out, has extensive experience in the field.
Azure ML is an amalgam of “new analytics tools, powerful algorithms developed for Microsoft products like Xbox and Bing,” he explained. “For customers, this means virtually none of the startup costs associated with authoring, developing and scaling machine learning solutions.”
To help businesses get up and running, Azure ML provides visual workflows and startup templates. Customers will be able collaborate more efficiently and publish APIs and Web services in mere minutes, added Sirosh, enabling them to “quickly turn analytic assets into enterprise-grade production cloud services,” said Sirosh.
The move is part of Microsoft’s effort to popularize and put a user-friendly spin on big data-driven cloud services. On Feb. 10, the company officially launched Power BI for Office 365. Julia White, general manager of Office product marketing, said at the time that is was the company’s goal to democratize “data availability to users” and bring “BI to a billion users.” (The Microsoft Office ecosystem boasts a billion worldwide users.)
Microsoft’s latest solution has already attracted a couple of early adopters, MAX451 and OSISoft in conjunction with Carnegie Mellon University. MAX451 is working on predicting retail customer buying behavior while OSISoft is working with the school on optimizing energy efficiency at its campus.
