Edge Computing Benefits for AI Crystallizing

| January 31, 2020

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Interest in edge computing continues to build, as does  confusion surrounding the architecture. The situation is similar when it comes to artificial intelligence. The prospect of moving AI to the edge might sound like a recipe for even more confusion. Performing artificial intelligence at the edge is often just theory quoted in articles, said Martin Davis, managing partner at DUNELM Associates.  Still, the concept of edge AI is increasingly hard for industrial and enterprise organizations to ignore. Resource-intensive operations such as deep learning and computer vision have traditionally taken place in centralized computing environments. But the growing availability of high-performance networking and computing hardware opens up the possibility to shift that activity a centralized cloud architecture to the [edge], as consultant Chaitan Sharma wrote. It won’t happen overnight, but it is inevitable. Gartner predicts that three-fourths of enterprise data to be processed at the edge by 2025, while Grand View Research predicts the edge computing market to expand at an annual rate of 54% to 2025.

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Mosaic has a decade of data science consulting experience. As one of the premier analytics consulting firms, we are steeped in delivering real world solutions, focusing our efforts on solving business challenges and delivering results, not just doing a fancy scientific study. Many of our customers tell us they have spent millions on collecting and storing data, but have not gotten the return-on-investment they thought they would due to a lack of expertise running analytics and data science projects.

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