--- title: "Manage Machine Learning resources | Grafana Cloud documentation" description: "Manage forecasts, holidays, outlier detectors, and their alerts with gcx." --- > For a curated documentation index, see [llms.txt](/llms.txt). For the complete documentation index, see [llms-full.txt](/llms-full.txt). # Manage Machine Learning resources Use `gcx ml` to manage Grafana Cloud Machine Learning resources. ## Prerequisites The following applies: - Your Grafana stack must have [Machine Learning](https://grafana.com/docs/grafana-cloud/ai-tools/machine-learning/) enabled. - You don’t need a separate provider token. The commands use the active Grafana credential through the Machine Learning plugin proxy. ## Inspect resources Forecasts are jobs in the Machine Learning API. Use the server IDs from the list output with `get`, `update`, and `delete`. shell ![Copy code to clipboard](https://grafana.com/media/images/icons/icon-copy-small-2.svg) Copy ```shell gcx ml jobs list gcx ml holidays list gcx ml outlier-detectors list gcx ml jobs get -o yaml ``` Use `--json` or `--jq` to select output fields. ### Configure the number of records in the output Lists fetch all records from the collection endpoint and show up to 50 records by default: - Use `--limit 0` to show all records. - Use `--limit 10` to show fewer records. A truncation notice appears on `stderr` when records are omitted. ## Apply a resource Use a resource example to create a manifest. Set the datasource UID and the query for your target stack: shell ![Copy code to clipboard](https://grafana.com/media/images/icons/icon-copy-small-2.svg) Copy ```shell gcx resources list-examples jobs.ml.ext.grafana.app --jq '.["ml.ext.grafana.app"].v1alpha1[0].example' -o json > job.json gcx ml jobs create -f job.json --dry-run gcx ml jobs create -f job.json gcx ml jobs update -f job.json ``` > Creating or updating a forecast job can start model training. `--dry-run` checks the input without a server mutation. The result reports that server validation is not available. ### Delete a resource Delete commands require confirmation or `--force`. ## Export and push manifests Jobs, holidays, and outlier detectors use the standard resource pipeline. The provider removes server IDs and connection fields from each spec. ### Target stack Each write uses the target stack URL. The server ID from the source stack is stored in `metadata.name`. If that ID doesn’t exist on the target, the pipeline matches the normalized value of `spec.name`. Each write resolves the numeric datasource ID from the UID on the target stack. Writes need read access to the selected datasource. Matching: - Converts names to lowercase slugs. - Updates one matching resource or creates a resource when there is no match. - Multiple matches are rejected. ### Push cross-stack Data source UIDs and job-to-holiday references remain in the spec. Set these references for the target stack before a cross-stack push. After a cross-stack push, pull from the target to save its assigned IDs: shell ![Copy code to clipboard](https://grafana.com/media/images/icons/icon-copy-small-2.svg) Copy ```shell gcx resources push -p ./ml --dry-run gcx resources push -p ./ml gcx resources pull jobs.ml.ext.grafana.app holidays.ml.ext.grafana.app outlierdetectors.ml.ext.grafana.app -p ./ml ``` ## Manage parent alerts Machine Learning alerts belong to a job or an outlier detector, and have no independent collection endpoint or resource adapter. Use the parent ID with the alert commands. shell ![Copy code to clipboard](https://grafana.com/media/images/icons/icon-copy-small-2.svg) Copy ```shell gcx ml jobs list-alerts gcx ml jobs get-alert -o yaml gcx ml jobs create-alert -f alert.yaml gcx ml jobs update-alert -f alert.yaml gcx ml jobs delete-alert --force gcx ml outlier-detectors list-alerts ``` The same alert operations are available under `outlier-detectors`. ### Alert objects Alert files contain the Machine Learning alert object, including its `title`. Use `get-alert` to export an alert object before you edit it. ### Outlier alerts Outlier alerts require a nonzero `window`, such as `5m`. Don’t set `threshold` or `anomalyCondition` for an outlier alert.