ml/

Closes one or more anomaly detection jobs.

Deletes a calendar.

Deletes scheduled events from a calendar.

Deletes anomaly detection jobs from a calendar.

Deletes an existing datafeed.

Deletes an existing data frame analytics job.

Deletes expired and unused machine learning data.

Deletes a filter.

Deletes forecasts from a machine learning job.

Deletes an existing 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 model alias that refers to the trained model

Estimates the model memory

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.

Returns information on how ML is using memory.

Retrieves information about model snapshots.

Gets stats for anomaly detection job model snapshot upgrades that are in progress.

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 inference model.

Retrieves usage information for trained inference models.

Evaluate a trained model.

Returns defaults and limits used by machine learning.

Opens one or more anomaly detection jobs.

Posts scheduled events in a calendar.

Previews a datafeed.

Previews that will be analyzed given a data frame analytics config.

Instantiates 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.

Creates an inference trained model.

Creates a new model alias (or reassigns an existing one) to refer to the trained model

Creates part of a trained model definition

Creates a trained model vocabulary

Resets an existing 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.

Start a trained model deployment.

Stops one or more datafeeds.

Stops one or more data frame analytics jobs.

Stop a trained model deployment.

Updates certain properties of a datafeed.

Updates certain properties of a data frame analytics job.

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

Updates certain properties of an anomaly detection job.

Updates certain properties of a snapshot.

Upgrades a given job snapshot to the current major version.

Validates an anomaly detection job.

Validates an anomaly detection detector.