Process manufacturers are investing significant resources in machine-learning (ML) to increase reliability, profitability and sustainability in their operations-saving millions of dollars in short periods of time through increased efficiency. As interest in ML grows, focus is rightfully placed on developing and enhancing the associated algorithms, but manufacturers must examine the results of these algorithms to ensure that ML efforts are successful. This is because operationalization of algorithms, not an algorithm itself, is the linchpin to providing meaningful value.
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