Inferring against incoming data using machine learning

As we learned in Chapter 4, Leveraging Insights and Managing Data on Elasticsearch, ingest pipelines can be used to transform, process, and enrich incoming documents before indexing. Ingest pipelines provide an inference processor to run new documents through a trained machine learning model to infer classification or regression results.

Follow these instructions to create and test an ingest pipeline to run inference using the trained machine learning model:

1. Create a new ingest pipeline as follows. model_id will defer across Kibana instances and can be retrieved from the model pane in the Data Frame Analytics tab on Kibana. model_id in this case is classification-request-payloads-1615680927179:

   PUT _ingest/pipeline/ml-malicious-request
   {
     "processors": [
       {
         "inference": {
           "model_id...