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Priscilla Parodi for Elastic

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Trained Models for Supervised Learning

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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, Kibana, Logstash and Beats) Machine Learning solution, aiming to introduce and exemplify the possibilities and options available, in addition to addressing the context and usability.

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