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Posted By Nayana SinghSenior Product Marketing Manager
A recent post on the New York Times’ Bits Blog shines a national light on something we’ve known around here for years: Seattle is a cloud-first city — and Microsoft is a cloud-first company. In fact, cloud computing was born right here, 20 years ago, in the Seattle area. As the post’s author, Quentin Hardy, puts it:
“The roots of Seattle’s strength in cloud computing, longtime observers say, goes back to the mid-1990s, when Microsoft, long the sole big power in Seattle tech, began doing extensive work in distributed computing, or making computers work together in problem solving. That concept is the root of what cloud computing is today: lots of computer servers working together for various tasks.”
From those early days right up until today, Microsoft continues to push the frontier of what’s possible in cloud technology. Earlier this week, we announced the upcoming public preview of Microsoft Azure Machine Learning (Azure ML), a fully managed cloud service for building predictive analytics solutions. With Azure ML, Microsoft customers and partners can build data-driven applications to predict, forecast and change future outcomes. Visit the machine learning blog to learn more.
This week’s announcement is the second of two recent Microsoft cloud innovations; in April, I blogged about the limited public preview of Microsoft Azure Intelligent Systems Service. This new service allows enterprises to extend the Microsoft Azure cloud across devices and sensors to securely capture vital data, analyze it with familiar Microsoft tools such as HDInsight and Power BI for Office 365, and use those new insights to create real, measurable business value. You can see Intelligent Systems Service at work in the London Underground by watching this video.
Intelligent Systems Service and Azure ML mark a critical expansion of the comprehensive Microsoft cloud-first data platform. It’s a tremendous leap forward — and we’re not through thinking in the cloud yet. I look forward to seeing what my colleagues build next to transform the way businesses — and, really, all of us — think about and extract tremendous value from data.