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Anomaly detection is the process of identifying when something deviates from the usual and expected. If an anomaly can be detected early enough, relevant corrective action can be taken to avoid serious consequences. As children, we have played the game of who can identify the oddities in a cleverly composed picture. This is anomaly detection at play. Engineers, scientists and technologists have historically counted on anomaly detection to prevent industrial accidents, stop financial fraud, intervene early to address health risks, etc. Traditionally, anomaly detection systems have relied on statistical techniques, predefined rules and/or human expertise. But these approaches have their limitations in terms of scalability, adaptability, and accuracy.

The number of real life, high value use cases for AI anomaly detection have grown a lot over the years and are expected to continue to grow. Advances in artificial intelligence (AI) are revolutionizing the field of anomaly detection. The result is improved accuracy, faster detection, reduced false positives, scalability and cost-effectiveness. This article will identify what is needed to implement such solutions and will also touch on some use cases for illustrative purposes.

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