[role="xpack"] [testenv="basic"] [[infer-trained-model-deployment]] = Infer trained model deployment API [subs="attributes"] ++++ Infer trained model deployment ++++ Evaluates a trained model. [[infer-trained-model-deployment-request]] == {api-request-title} `POST _ml/trained_models//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} ``:: (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} `input`:: (Required,string) The input text for evaluation. //// [[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 { "input": "The movie was awesome!!" } -------------------------------------------------- // TEST[skip:TBD] The API returns scores in this case, for example: [source,console-result] ---- { "positive" : 0.9998062667902223, "negative" : 1.9373320977752957E-4 } ---- // NOTCONSOLE For named entity recognition (NER) tasks, the response contains the recognized entities and their type. For example: [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 scores in this case, for example: [source,console-result] ---- { "entities" : [ { "label" : "person", "score" : 0.9988716330253505, "word" : "Josh" }, { "label" : "location", "score" : 0.9980872542990656, "word" : "Berlin" } ] } ---- // NOTCONSOLE