elasticsearch/docs/reference/ml/df-analytics/apis/infer-trained-model-deployment.asciidoc

[role="xpack"]
[[infer-trained-model-deployment]]
= Infer trained model deployment API
[subs="attributes"]
++++
<titleabbrev>Infer trained model deployment</titleabbrev>
++++

Evaluates a trained model.

[[infer-trained-model-deployment-request]]
== {api-request-title}

`POST _ml/trained_models/<model_id>/deployment/_infer`

////
[[infer-trained-model-deployment-prereq]]
== {api-prereq-title}

////
////
[[infer-trained-model-deployment-desc]]
== {api-description-title}

////

[[infer-trained-model-deployment-path-params]]
== {api-path-parms-title}

`<model_id>`::
(Required, string)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=model-id]

[[infer-trained-model-deployment-query-params]]
== {api-query-parms-title}

`timeout`::
(Optional, time)
Controls the amount of time to wait for {infer} results. Defaults to 10 seconds.

[[infer-trained-model-request-body]]
== {api-request-body-title}

`docs`::
(Required, array)
An array of objects to pass to the model for inference. The objects should
contain a field matching your configured trained model input. Typically, the field
name is `text_field`. Currently, only a single value is allowed.

////
[[infer-trained-model-deployment-results]]
== {api-response-body-title}
////
////
[[ml-get-trained-models-response-codes]]
== {api-response-codes-title}

////

[[infer-trained-model-deployment-example]]
== {api-examples-title}

The response depends on the task the model is trained for. If it is a
text classification task, the response is the score. For example:

[source,console]
--------------------------------------------------
POST _ml/trained_models/model2/deployment/_infer
{
	"docs": [{"text_field": "The movie was awesome!!"}]
}
--------------------------------------------------
// TEST[skip:TBD]

The API returns the predicted label and the confidence.

[source,console-result]
----
{
  "predicted_value" : "POSITIVE",
  "prediction_probability" : 0.9998667964092964
}
----
// NOTCONSOLE

For named entity recognition (NER) tasks, the response contains the annotated
text output and the recognized entities.

[source,console]
--------------------------------------------------
POST _ml/trained_models/model2/deployment/_infer
{
	"input": "Hi my name is Josh and I live in Berlin"
}
--------------------------------------------------
// TEST[skip:TBD]

The API returns in this case:

[source,console-result]
----
{
  "predicted_value" : "Hi my name is [Josh](PER&Josh) and I live in [Berlin](LOC&Berlin)",
  "entities" : [
    {
      "entity" : "Josh",
      "class_name" : "PER",
      "class_probability" : 0.9977303419824,
      "start_pos" : 14,
      "end_pos" : 18
    },
    {
      "entity" : "Berlin",
      "class_name" : "LOC",
      "class_probability" : 0.9992474323902818,
      "start_pos" : 33,
      "end_pos" : 39
    }
  ]
}
----
// NOTCONSOLE