In Kibana, you can export and import your machine learning job and datafeed configuration details in Stack Management > Machine Learning Jobs. For example, you can export jobs from your test environment and import them into your production environment.
The exported file contains configuration details; it does not contain the machine learning models. For anomaly detection, you must import and run the job to build a model that is accurate for the new environment. For data frame analytics, trained models are portable and can be transferred between clusters as described in Exporting and importing models.
There are some additional actions that you must take before you can successfully import and run your jobs:
- The Kibana data views that are used by anomaly detection datafeeds and data frame analytics source indices must exist; otherwise, the import fails.
- If your anomaly detection jobs use custom rules with filter lists, the filter lists must exist; otherwise, the import fails. To create filter lists, use Kibana or the create filters API.
- If your anomaly detection jobs were associated with calendars, you must create the calendar in the new environment and add your imported jobs to the calendar. Use Kibana or the create calendars, add events to calendar, and add jobs to calendar APIs.