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[Case study] How to optimize energy investments

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With industry changes such as smart meter and renewable energy adoption, utilities companies needed to make data-driven decisions to improve efficiency and cut cost. This results in a completely new way of visualizing data, while helping their customers make decisions faster to save resources and costs. To solve new energy industry problems, eSmart Systems decided to use deep learning to find problems automatically; use drones as "the eye in the the sky"; develop a tool for field crew that makes their job easier and safer; and attempt to predict problems before they turn into critical errors. Machine learning and analytics help them better understand what is about to happen because they have established a timeline with their very broad definition of time series. Download this case study to understand better how eSmart Systems uses MS Azure and InfluxDB Enterprise to optimize energy investments.


How eSmart Systems and Microsoft Azure are helping utility companies

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Learn how Microsoft Partner, eSmart Systems, empowers utility companies to stay ahead of power grid maintenance issues. Maintenance of electrical grids is not only time consuming and costly, but it can also be very dangerous. By developing a connected drone that uses AI and cognitive services from Microsoft Azure, utility companies can reduce blackouts and inspect power lines more safely. Learn how eSmart Systems' innovation with Microsoft is helping utility companies better protect their personnel and serve their communities. To learn about more areas where your agency can benefit from AI, read the Gartner report: https://aka.ms/whereyoushoulduseAI


Democratizing Machine Learning

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It used to be that one great technology defined an era. The steam engine, for example, served as the catalyst for the rise of the industrial age. Nowadays, however, a number of amazing technical advances and inventions are contending for bragging rights as the leading technology of our times. I would argue that one is particularly worthy of such boasting: machine learning. Although it has been in slow and steady development for years and has been used in a few enterprise applications, it has recently burst onto the scene in response to the explosion of data in today's increasingly connected digital world.