Carnegie Mellon University (CMU) uses Microsoft Azure and the PI System™ from Microsoft Global ISV partner OSIsoft to reduce building maintenance and energy costs. In addition to Azure, now CMU has added Azure Machine Learning for better fault detection, diagnosis, and more efficient operations. With these capabilities, CMU personnel gain advanced analytics for improved operational insights and decisions and they cut energy use by 20 percent.
Azure Machine Learning simplified and accelerated the time-consuming process of creating and testing machine learning models. A typically weeks-long process was accomplished in a few days.
“We immediately began using Azure Machine Learning without having to prepare on-premises software; everything’s ready-to-use in the cloud,” says Lasternas. “It’s significantly easier to use than other tools we’ve tried, and it fit seamlessly with the PI System and Microsoft cloud solution we already had.”
The solution also fosters collaboration. “We can easily collaborate by sharing workspaces,” says Yogesh Venkata Gopalan, Graduate Student, Energy Science Technology and Policy, Carnegie Mellon University.
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