Amazon Lookout for Metrics uses machine learning (ML) to detect anomalies in virtually any time- series business or operational metrics–such as revenue performance, purchase transactions, and customer acquisition and retention rates–with no ML experience required.
ML-powered anomaly detection
Amazon Lookout for Metrics monitors metrics and detects anomalies with high accuracy using ML technology. It uses specialized ML models to detect anomalies based on the characteristics of your data. You don’t need ML experience to use Amazon Lookout for Metrics.
Anomaly Grouping and Ranking
Amazon Lookout for Metrics automatically 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 continues to increase over time as you provide more feedback.
Data source compatibility
Amazon Lookout for Metrics seamlessly connects to popular data sources, including Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Relational Database Service (Amazon RDS), Amazon CloudWatch, Salesforce, Marketo, Dynatrace, Singular, Zendesk, Servicenow, Infor Nexus, Trendmicro, Veeva, Google Analytics, and Amplitude.
Custom, automated alerts
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, Datadog, PagerDuty, Webhooks, and Slack. You can create custom alerts to notify you when Amazon Lookout for Metrics detects an anomaly of a specified severity level.
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