This functionality is experimental and may be changed or removed completely in a future release. Elastic will take a best effort approach to fix any issues, but experimental features are not subject to the support SLA of official GA features.
When you use a data frame analytics job to perform classification or regression analysis, it creates a machine learning model that is trained and tested against a labelled data set. When you are satisfied with your trained model, you can use it to make predictions against new data. For example, you can use it in the processor of an ingest pipeline or in a pipeline aggregation within a search query. For more information about this process, see Introduction to supervised learning and Inference.
You can also supply trained models that are not created by data frame analytics job but adhere to the appropriate JSON schema. If you want to use these trained models in the Elastic Stack, you must store them in Elasticsearch documents by using the create trained models API.
In Kibana, you can view and manage your trained models within Machine Learning > Data Frame Analytics: