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éléments par Ira Cohen

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  6 design principles for machine learning anomaly detection systems

  Publié: août 5, 2019, 7:18pm CEST par Ira Cohen

  Every year, 22 percent of eCommerce customers abandon their shopping carts due to website errors. Every year, insurance companies discharge up to 10 percent of their claims cost on fraudulent claims. Network outages cost up to $5,600 per minute. These and other failures represent anomalies that are detectable by machine learning in ways that human-powered monitoring can’t replicate. When it comes to deploying a machine learning anomaly detection system, companies have the choice of either purchasing a ready-made system or developing their own. No matter what they choose, however, the resulting system should be based on criteria that account for… [Continue Reading]