Amazon Technologies, Inc.

Vikas Dharia

Jasmeet Chhabra

Harshad Vasant Kulkarni

Umut Orhan

Khaled Salah Sedky

Scott Michael Wiltamuth

Zaid Radi Abu Ziad

Douglas Allen Walter

Jan Gasthaus

Tim Januschowski

Valentin Flunkert

Rahul Tongia

A plurality of metrics records, including some records indicating metrics for which anomaly analysis has been performed, is obtained. Using a training data set which includes the metrics records, a machine learning model is trained to predict an anomaly analysis relevance score for an input record which indicates a metric name. Collection of a particular metric of an application is initiated based at least in part on an anomaly analysis relevance score obtained for the particular metric using a trained version of the model.

Machine learning-based selection of metrics for anomaly detection

Techniques for determining insight are described. An exemplary method includes receiving a request to provide insight into potential abnormal behavior; receiving one or more of anomaly information and event information associated with the potential abnormal behavior; evaluating the received one or more of the anomaly information and event information associated with the abnormal behavior to determine there is insight as to what is causing the potential abnormal behavior and to add to an insight at least two of an indication of a metric involved in the abnormal behavior, a severity for the insight indication, an indication of a relevant event involved in the abnormal behavior, and a recommendation on how to cure the potential abnormal behavior; and providing an insight indication for the generated insight.

Systems, methods, and apparatuses for detecting and creating operation incidents

Growing community of inventors

Sammamish, WA, United States of America

Vikas Dharia