Supplied configurations
Elastic Cloud Serverless projects are autoscaled environments, fully managed by Elastic and available on Elastic Cloud. Availability This functionality is generally available and ready for production usage. Serverless interfaces and procedures might differ from classic Elastic Stack deployments. The Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats. Availability This functionality is generally available and ready for production usage. Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments. This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.
Serverless
Generally available
Stack
Generally available
Anomaly detection jobs contain the configuration information and metadata necessary to perform an analytics task. Kibana can recognize certain types of data and provide specialized wizards for that context. This page lists the categories of the anomaly detection jobs that are ready to use via Kibana in Machine learning. Refer to Create anomaly detection jobs to learn more about creating a job by using supplied configurations. Logs and Metrics supplied configurations are available and can be created via the related solution UI in Kibana.
The configurations are only available if data exists that matches the queries specified in the manifest files. These recognizer queries are linked in the descriptions of the individual configurations.
By default, these jobs have model_memory_limit values that are deemed appropriate for typical user environments and data characteristics. If your environment or your data is atypical and your jobs reach a memory status value of soft_limit or hard_limit, you might need to update the model memory limits. For more information, see Working with anomaly detection at scale.