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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.

To see your available models: Kibana>Machine Learning>Data Frame Analytics>Models

Alternatively, you can use APIs like get trained models.

The following example gets information for all the trained models:

GET _ml/trained_models/

Models trained in Elasticsearch are portable and can be transferred between clusters.

It is also possible to import a model to your Elasticsearch cluster even if the model is not trained by Elastic Data Frame analytics. Eland supports importing models directly through its APIs.

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This post is part of a series that covers Artificial Intelligence with a focus on Elastic's (Creators of Elasticsearch) Machine Learning solution, aiming to introduce and exemplify the possibilities and options available, in addition to addressing the context and usability.