US Patent 10102056 Anomaly detection using machine learning

A machine learning engine is configured to create a customized anomaly detector for use by a system resource, such as a virtual machine instance running specific operations. Anomalies are determined by comparing aspects of current data to “normal” baseline data indicating a normal range of performance and operation of a system resource. Operation by a system resource that is outside of this normal range of performance and operation may be considered an anomaly. The machine learning engine may be used to create customized monitoring that detects anomalies in operation and/or performance of a system resource based on at least some custom parameters that are unique to the particular system that is to be monitored. The parameters may include operational parameters, which may be selected specifically for the system to be monitored. The parameters may also include some technical parameters which are used by many other systems to monitor hardware performance.