amazon Lookout for Metrics is a fully managed service that 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. , without the need for ML experience. . The service, which launched in March 2021, predates several popular AWS offerings that have anomaly detection, including amazon OpenSearch, amazon CloudWatch, AWS Glue Data Quality, amazon Redshift ML, and amazon QuickSight.
After careful consideration, we made the decision to end support for amazon Lookout for Metrics, effective October 10, 2025. Additionally, starting today, new customer registrations are no longer available. Existing customers will be able to use the service as usual until October 10, 2025, when we will end support for amazon Lookout for Metrics.
In this post, we provide an overview of alternative AWS services that offer anomaly detection capabilities for customers to consider transitioning their workloads.
AWS services with anomaly detection capabilities
We recommend customers use amazon OpenSearch, amazon CloudWatch, amazon Redshift ML, amazon QuickSight, or AWS Glue Data Quality services for their anomaly detection use cases as an alternative to amazon Lookout for Metrics. These AWS services provide ML-based anomaly detection capabilities that are generally available and can be used out of the box without requiring ML expertise. Below is a brief description of each service.
Using amazon OpenSearch for Anomaly Detection
amazon OpenSearch Service features an integrated, high-performance anomaly detection engine that enables real-time identification of anomalies in streaming data as well as historical data. You can combine anomaly detection with alerts built into OpenSearch to send notifications when there is an anomaly. To start using OpenSearch for anomaly detection, you must first index your data in OpenSearch, from there you can enable anomaly detection in OpenSearch dashboards. For more information, see the documentation.
Using amazon CloudWatch for Anomaly Detection
amazon CloudWatch supports creating anomaly detectors on specific amazon CloudWatch log groups by applying statistical and machine learning algorithms to CloudWatch metrics. Anomaly detection alarms can be created based on the expected value of a metric. These types of alarms do not have a static threshold to determine the alarm status. Instead, they compare the value of the metric with the expected value based on the anomaly detection model. To start using CloudWatch anomaly detection, you must first ingest data into CloudWatch and then enable anomaly detection on the log group.
Using amazon Redshift ML for Anomaly Detection
amazon Redshift ML makes it easy to create, train, and apply machine learning models using familiar SQL commands in amazon Redshift data warehouses. Anomaly detection can be performed on your analytics data through Redshift ML using the included XGBoost model type, local models, or remote models with amazon SageMaker. With Redshift ML, you don't need to be a machine learning expert and only pay for the cost of training SageMaker models. There are no additional costs for using Redshift ML for anomaly detection. For more information, see the documentation.
Using amazon QuickSight for Anomaly Detection
amazon QuickSight is a fast, cloud-based business intelligence service that delivers insights to everyone in your organization. As a fully managed service, QuickSight allows customers to create and publish interactive dashboards that include machine learning insights. QuickSight supports an integrated, high-performance anomaly detection engine that uses proven amazon technology to continuously run ML-based anomaly detection on millions of metrics to uncover hidden trends and outliers in customer data. This tool allows clients to obtain detailed information that is often hidden in aggregates and is not scalable with manual analysis. With ML-based anomaly detection, customers can find outliers in their data without the need for manual analysis, custom development, or ML domain expertise. For more information, see the documentation.
Using amazon Glue Data Quality for Anomaly Detection
Data engineers and analysts can use AWS Glue Data Quality to measure and monitor their data. AWS Glue Data Quality uses a rules-based approach that works well for known data patterns and provides ML-based recommendations to help you get started. You can review recommendations and augmentation rules for over 25 included data quality rules. To capture unexpected and less obvious data patterns, you can enable anomaly detection. To use this feature, you can write rules or parsers and then enable anomaly detection in AWS Glue ETL. AWS Glue Data Quality collects statistics for the columns specified in rules and analyzers, applies machine learning algorithms to detect anomalies, and generates visual observations that explain detected problems. Customers can use recommended rules to capture anomalous patterns and provide feedback to tune the machine learning model for more accurate detection. For more information, check out the blog post, watch the introductory videoor consult the documentation.
Using amazon SageMaker Canvas for Anomaly Detection (a beta feature)
The amazon SageMaker Canvas team plans to support anomaly detection use cases in amazon SageMaker Canvas. We've created an AWS CloudFormation template-based solution to give customers early access to the underlying anomaly detection functionality. Customers can use the CloudFormation template to open an application stack that receives time series data from an amazon Managed Streaming for Apache Kafka (amazon MSK) streaming source and performs near real-time anomaly detection on the data. streaming. For more information about the beta offering, see Anomaly Detection in Streaming Time Series Data with Online Learning Using amazon Managed Service for Apache Flink.
Frequently asked questions
- What is the cut-off point for current customers?
We create a list of allowed account IDs that have used amazon Lookout for Metrics in the last 30 days and have active amazon Lookout for Metrics resources, including listeners, within the service. If you are an existing customer and are having difficulty using the service, please contact us through AWS Customer Support for assistance.
- As Will access change before the expiration date?
Today's customers can do all the things they could do before. The only change is that non-current customers cannot create any new resources in amazon Lookout for Metrics.
- What happens to my amazon Lookout for Metrics resources after the expiration date?
After October 10, 2025, all references to AWS Lookout for Metrics models and resources will be removed from amazon Lookout for Metrics. You will not be able to discover or access amazon Lookout for Metrics from your AWS Management Console, and applications that call the amazon Lookout for Metrics API will no longer work.
- Will I be billed for amazon Lookout for Metrics resources remaining in my account after October 10, 2025?
Resources created internally by amazon Lookout for Metrics will be deleted after October 10, 2025. Customers will be responsible for deleting input data sources they create, such as amazon Simple Storage Service (amazon S3) buckets, clusters from amazon Redshift, etc. .
- How do I delete my resources from amazon Lookout for Metrics?
- How can I export failure data before deleting resources?
Anomaly data for each measurement can be downloaded for a detector using the amazon Lookout for Metrics APIs for a particular detector. amazon-lookout-for-metrics-samples/blob/main/getting_started/4.ExportingAnomalies.ipynb” target=”_blank” rel=”noopener”>Anomaly export explains how to connect to a detector, query anomalies, and download them in a format for later use.
Conclusion
In this blog post, we describe methods for creating anomaly detectors using alternatives such as amazon OpenSearch, amazon CloudWatch, and a CloudFormation template-based solution.
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About the author
Nirmal Kumar is a senior product manager for the amazon SageMaker service. Committed to expanding access to ai/ML, he leads the development of no-code and low-code ML solutions. Outside of work, she enjoys traveling and reading nonfiction.
