ml/

Clears a trained model deployment cache on all nodes where the trained model is assigned.

Close anomaly detection jobs A job can be opened and closed multiple times throughout its lifecycle.

Removes all scheduled events from a calendar, then deletes it.

Deletes scheduled events from a calendar.

Deletes anomaly detection jobs from a calendar.

Deletes an existing datafeed.

Deletes a data frame analytics job.

Deletes expired and unused machine learning data.

Deletes a filter.

Deletes forecasts from a machine learning job.

Deletes an anomaly detection job.

Deletes an existing model snapshot.

Deletes an existing trained inference model that is currently not referenced by an ingest pipeline.

Deletes a trained model alias.

Makes an estimation of the memory usage for an anomaly detection job model.

Evaluates the data frame analytics for an annotated index.

Explains a data frame analytics config.

Forces any buffered data to be processed by the job.

Predicts the future behavior of a time series by using its historical behavior.

Retrieves anomaly detection job results for one or more buckets.

Retrieves information about the scheduled events in calendars.

Retrieves configuration information for calendars.

Retrieves anomaly detection job results for one or more categories.

Retrieves configuration information for datafeeds.

Retrieves usage information for datafeeds.

Retrieves configuration information for data frame analytics jobs.

Retrieves usage information for data frame analytics jobs.

Retrieves filters.

Retrieves anomaly detection job results for one or more influencers.

Retrieves configuration information for anomaly detection jobs.

Retrieves usage information for anomaly detection jobs.

Get information about how machine learning jobs and trained models are using memory, on each node, both within the JVM heap, and natively, outside of the JVM.

Retrieves information about model snapshots.

Retrieves usage information for anomaly detection job model snapshot upgrades.

Retrieves overall bucket results that summarize the bucket results of multiple anomaly detection jobs.

Retrieves anomaly records for an anomaly detection job.

Retrieves configuration information for a trained model.

Retrieves usage information for trained models.

Evaluates a trained model.

Returns defaults and limits used by machine learning.

Opens one or more anomaly detection jobs.

Adds scheduled events to a calendar.

Sends data to an anomaly detection job for analysis.

Previews a datafeed.

Previews the extracted features used by a data frame analytics config.

Creates a calendar.

Adds an anomaly detection job to a calendar.

Instantiates a datafeed.

Instantiates a data frame analytics job.

Instantiates a filter.

Instantiates an anomaly detection job.

Enables you to supply a trained model that is not created by data frame analytics.

Creates or updates a trained model alias.

Creates part of a trained model definition.

Creates a trained model vocabulary.

Resets an anomaly detection job.

Reverts to a specific snapshot.

Sets a cluster wide upgrade_mode setting that prepares machine learning indices for an upgrade.

Starts one or more datafeeds.

Starts a data frame analytics job.

Starts a trained model deployment, which allocates the model to every machine learning node.

Stops one or more datafeeds.

Stops one or more data frame analytics jobs.

Stops a trained model deployment.

Updates the properties of a datafeed.

Updates an existing data frame analytics job.

Updates the description of a filter, adds items, or removes items from the list.

Updates certain properties of an anomaly detection job.

Updates certain properties of a snapshot.

Starts a trained model deployment, which allocates the model to every machine learning node.

Upgrades an anomaly detection model snapshot to the latest major version.

Validates an anomaly detection job.

Validates an anomaly detection detector.