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Rethinking Anomaly Detection

John Sipple, Staff Software Engineer in AI, at Google Cloud presents Google's story about rethinking anomaly detection. In 2019, Google Smart Buildings asked the team to develop an AI-based fault-detection solution to help find and fix problems in climate control devices in large office buildings. Technicians were dissatisfied with conventional outlier approaches because they didn’t give the necessary insight to predict, diagnose and intervene. The result was a distributed deep-learning solution that provides explanations to aid understanding, prioritizing and fixing faults. We applied it to other domains, like data center monitoring and fraud detection, and then open-sourced the MADI machine learning algorithm behind it. We’ll describe our vision of how AI will shape the future of interpretable anomaly detection.


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