--- title: stack es ml cli namespace description: Elasticsearch ml API commands url: https://www.elastic.co/elastic/docs-builder/docs/4083/reference/elastic-cli/cli/stack/es/ml applies_to: - Elastic Cloud Serverless: Preview - Elastic Stack: Preview --- # stack es ml cli namespace ```bash elastic stack es ml --help ``` Elasticsearch ml API commands ## Commands Clear trained model deployment cache. Close anomaly detection jobs. Delete a calendar. Delete events from a calendar. Delete anomaly jobs from a calendar. Delete a data frame analytics job. Delete a datafeed. Delete expired ML data. Delete a filter. Delete forecasts from a job. Delete an anomaly detection job. Delete a model snapshot. Delete an unreferenced trained model. Delete a trained model alias. Estimate job model memory usage. Evaluate data frame analytics. Explain data frame analytics config. Force buffered data to be processed. Predict future behavior of a time series. Get anomaly detection job results for buckets. Get info about events in calendars. Get calendar configuration info. Get anomaly detection job results for categories. Get data frame analytics job configuration info. Get data frame analytics job stats. Get datafeed stats. Get datafeeds configuration info. Get filters. Get anomaly detection job results for influencers. Get anomaly detection job stats. Get anomaly detection jobs configuration info. Get machine learning memory usage info. Get anomaly detection job model snapshot upgrade usage info. Get model snapshots info. Get overall bucket results. Get anomaly records for an anomaly detection job. Get trained model configuration info. Get trained models usage info. Evaluate a trained model. Get machine learning information. Open anomaly detection jobs. Add scheduled events to the calendar. Send data to an anomaly detection job for analysis. Preview features used by data frame analytics. Preview a datafeed. Create a calendar. Add anomaly detection job to calendar. Create a data frame analytics job. Create a datafeed. Create a filter. Create an anomaly detection job. Create a trained model. Create or update a trained model alias. Create part of a trained model definition. Create a trained model vocabulary. Reset an anomaly detection job. Revert to a snapshot. Set upgrade_mode for ML indices. Start a data frame analytics job. Start datafeeds. Start a trained model deployment. Stop data frame analytics jobs. Stop datafeeds. Stop a trained model deployment. Update a data frame analytics job. Update a datafeed. Update a filter. Update an anomaly detection job. Update a snapshot. Update a trained model deployment. Upgrade a snapshot.