Inference for Supervised Learning

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Inference is a machine learning feature that enables you to use supervised data frame analytics model – Regression and Classification or a model deployed for Natural Language Processing – against incoming data.

Let's assume that you have an index with historical data and a classification model that is trained on this data and that you are receiving new data, with inference you can perform the classification against the new data with the same input fields that you've trained the model on, and get a prediction.

All you need to do is create an ingest pipeline with a configurable inference processor:

Kibana>Management>Stack Management>Ingest>Ingest Node Pipelines. Click on Create Pipeline.

And add your inference processor:

Add a processor

Processor = Inference>Add

And add the target field (optional) ml.inference.<results_field_name> and the ID of your trained model.

Note: After the Elastic Data Frame - Classification Analysis HandsOn(prerequisite) we have an example using the inference processor.

But now, let's proceed with the Classification Analysis HandsOn.

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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.