Run Athena ML Query

From the Athena Console, make sure that you are switched to V2EngineWorkGroup. This workgroup is configured with the Athena engine version 2, which will enable Athena ML capabilities for your query. Run the saved query “DetectAnamoliesInOrdersData” by replacing the endpoint name with the one that you generated from your SageMaker notebook run.
As you can see from the results, With each data point, Random Cut Forest algorithm associates an anomaly score. Low score values indicate that the data point is considered “normal.” High values indicate the presence of an anomaly in the data. The definitions of “low” and “high” depend on the application but common practice suggests that scores beyond three standard deviations from the mean score are considered anomalous.