Amazon Lookout for Metrics is a absolutely managed service that makes use of machine studying (ML) to detect anomalies in just about any time-series enterprise or operational metrics—resembling income efficiency, buy transactions, and buyer acquisition and retention charges—with no ML expertise required. The service, which was launched in March 2021, predates a number of well-liked AWS choices which have anomaly detection, resembling Amazon OpenSearch, Amazon CloudWatch, AWS Glue Information High quality, Amazon Redshift ML, and Amazon QuickSight.
After cautious consideration, now we have made the choice to finish help for Amazon Lookout for Metrics, efficient October 10, 2025. As well as, as of right this moment, new buyer sign-ups are not obtainable. Present clients will be capable to use the service as regular till October 10, 2025, after we will finish help for Amazon Lookout for Metrics.
On this publish, we offer an outline of the alternate AWS companies that provide anomaly detection capabilities for patrons to think about transitioning their workloads to.
AWS companies with anomaly detection capabilities
We suggest clients use Amazon OpenSearch, Amazon CloudWatch, Amazon Redshift ML, Amazon QuickSight, or AWS Glue Information High quality companies for his or her anomaly detection use circumstances as a substitute for Amazon Lookout for Metrics. These AWS companies supply typically obtainable, ML-powered anomaly detection capabilities that can be utilized out of the field with out requiring any ML experience. Following is a short overview of every service.
Utilizing Amazon OpenSearch for anomaly detection
Amazon OpenSearch Service contains a extremely performant, built-in anomaly detection engine that allows the real-time identification of anomalies in streaming information in addition to in historic information. You possibly can pair anomaly detection with built-in alerting in OpenSearch to ship notifications when there’s an anomaly. To begin utilizing OpenSearch for anomaly detection you first should index your information into OpenSearch, from there you possibly can allow anomaly detection in OpenSearch Dashboards. To be taught extra, see the documentation.
Utilizing Amazon CloudWatch for anomaly detection
Amazon CloudWatch helps creating anomaly detectors on particular Amazon CloudWatch Log Teams by making use of statistical and ML algorithms to CloudWatch metrics. Anomaly detection alarms may be created primarily based on a metric’s anticipated worth. Some of these alarms don’t have a static threshold for figuring out alarm state. As a substitute, they examine the metric’s worth to the anticipated worth primarily based on the anomaly detection mannequin. To begin utilizing CloudWatch anomaly detection, you first should ingest information into CloudWatch after which allow anomaly detection on the log group.
Utilizing Amazon Redshift ML for anomaly detection
Amazon Redshift ML makes it straightforward to create, practice, and apply machine studying fashions utilizing acquainted SQL instructions in Amazon Redshift information warehouses. Anomaly detection may be accomplished in your analytics information by means of Redshift ML by utilizing the included XGBoost mannequin kind, native fashions, or distant fashions with Amazon SageMaker. With Redshift ML, you don’t need to be a machine studying professional and also you pay just for the coaching price of the SageMaker fashions. There aren’t any extra prices to utilizing Redshift ML for anomaly detection. To be taught extra, see the documentation.
Utilizing Amazon QuickSight for anomaly detection
Amazon QuickSight is a quick, cloud-powered, enterprise intelligence service that delivers insights to everybody within the group. As a completely managed service, QuickSight lets clients create and publish interactive dashboards that embody ML insights. QuickSight helps a extremely performant, built-in anomaly detection engine that makes use of confirmed Amazon expertise to repeatedly run ML-powered anomaly detection throughout thousands and thousands of metrics to find hidden developments and outliers in clients’ information. This instrument permits clients to get deep insights which might be typically buried within the aggregates and never scalable with guide evaluation. With ML-powered anomaly detection, clients can discover outliers of their information with out the necessity for guide evaluation, customized growth, or ML area experience. To be taught extra, see the documentation.
Utilizing Amazon Glue Information High quality for anomaly detection
Information engineers and analysts can use AWS Glue Information High quality to measure and monitor their information. AWS Glue Information High quality makes use of a rule-based method that works properly for recognized information patterns and gives ML-based suggestions that will help you get began. You possibly can assessment the suggestions and increase guidelines from over 25 included information high quality guidelines. To seize unanticipated, much less apparent information patterns, you possibly can allow anomaly detection. To make use of this characteristic, you possibly can write guidelines or analyzers after which activate anomaly detection in AWS Glue ETL. AWS Glue Information High quality collects statistics for columns laid out in guidelines and analyzers, applies ML algorithms to detect anomalies, and generates visible observations explaining the detected points. Clients can use advisable guidelines to seize the anomalous patterns and supply suggestions to tune the ML mannequin for extra correct detection. To be taught extra, see the weblog publish, watch the introductory video, or see the documentation.
Utilizing Amazon SageMaker Canvas for anomaly detection (a beta characteristic)
The Amazon SageMaker Canvas crew plans to offer help for anomaly detection use circumstances in Amazon SageMaker Canvas. We’ve created an AWS CloudFormation template-based answer to present clients early entry to the underlying anomaly detection characteristic. Clients can use the CloudFormation template to convey up an utility stack that receives time-series information from an Amazon Managed Streaming for Apache Kafka (Amazon MSK) streaming supply and performs near-real-time anomaly detection within the streaming information. To be taught extra concerning the beta providing, see Anomaly detection in streaming time collection information with on-line studying utilizing Amazon Managed Service for Apache Flink.
Often requested questions
- What’s the cutoff level for present clients?
We created an permit record of account IDs which have used Amazon Lookout for Metrics within the final 30 days and have lively Amazon Lookout for Metrics sources, together with detectors, throughout the service. If you’re an present buyer and are having difficulties utilizing the service, please attain out to us by way of AWS Buyer Assist for assist.
- How will entry change earlier than the sundown date?
Present clients can do all of the issues they may beforehand. The one change is that non-current clients can’t create any new sources in Amazon Lookout for Metrics.
- What occurs to my Amazon Lookout for Metrics sources after the sundown date?
After October 10, 2025, all references to AWS Lookout for Metrics fashions and sources might be deleted from Amazon Lookout for Metrics. You will be unable to find or entry Amazon Lookout for Metrics out of your AWS Administration Console and purposes that decision the Amazon Lookout for Metrics API will not work.
- Will I be billed for Amazon Lookout for Metrics sources remaining in my account after October 10, 2025?
Assets created by Amazon Lookout for Metrics internally might be deleted after October 10, 2025. Clients might be chargeable for deleting the enter information sources created by them, resembling Amazon Easy Storage Service (Amazon S3) buckets, Amazon Redshift clusters, and so forth.
- How do I delete my Amazon Lookout for Metrics sources?
- How can I export anomalies information earlier than deleting the sources?
Anomalies information for every measure may be downloaded for a detector by utilizing the Amazon Lookout for Metrics APIs for a selected detector. Exporting Anomalies explains how to hook up with a detector, question for anomalies, and obtain them right into a format for later use.
Conclusion
On this weblog publish, now we have outlined strategies to create anomaly detectors utilizing alternates resembling Amazon OpenSearch, Amazon CloudWatch, and a CloudFormation template-based answer.
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Concerning the Writer
Nirmal Kumar is Sr. Product Supervisor for the Amazon SageMaker service. Dedicated to broadening entry to AI/ML, he steers the event of no-code and low-code ML options. Exterior work, he enjoys travelling and studying non-fiction.
