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Carnegie Mellon Sees a Way to Cut Energy Use by 20 Percent with Cloud Machine Learning Solution

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Last February we wrote about how Carnegie Mellon University improves operational efficiency, cutsCMU energy consumption by using Power BI and the PI System from our partner OSIsoft. Now Carnegie Mellon has added Azure Machine Learning leveraging the PI System for better fault detection, diagnosis, and more efficient operations. With these capabilities CMU is gaining advanced analytics for improved operational insights, decision making and finding new ways to cut energy usage.

It’s a compelling story about how Azure ML is providing speed to value and self-service predictive analytics for the masses. As Bertrand Lasternas of Carnegie Mellon says: "The ease of implementation makes machine learning accessible to a larger number of investigators with various backgrounds and even non-data scientists."

We encourage you to take a look at the full case study, Carnegie Mellon Sees a Way to Cut Energy Use by 20 Percent with Cloud Machine Learning Solution, and learn firsthand how Azure ML is the future of Analytics. May the Cloud be with You! – Jon C. Arnold