Amazon Lookout For Metrics Documentation

Amazon 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.

Amazon 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. 

Amazon 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 Amazon 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.

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

After Amazon Lookout for Metrics creates an anomaly detection model, or detector, you can attach alerts to it using supported output connectors such as Amazon Simple Notification Service (Amazon SNS), AWS Lambda functions, and supported third party output connectors. You can create custom alerts to notify you when Amazon 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 https://docs.aws.amazon.com/index.html. This additional information does not form part of the Documentation for purposes of the AWS Customer Agreement available at http://aws.amazon.com/agreement, or other agreement between you and AWS governing your use of AWS’s services.