Stop a trained model deployment

Required authorization

Authorization: <value>

Elasticsearch APIs use key-based authentication. You must create an API key and use the encoded value in the request header. For example:

		curl -X GET "${ES_URL}/_cat/indices?v=true" \
  -H "Authorization: ApiKey ${API_KEY}"

For more information about where to find API keys for the Elasticsearch endpoint (${ES_URL}) for a project, go to Get started with Elasticsearch Serverless.

application/json

Examples

Request Console 1 / 8 languages

Console Kibana Dev Tools

POST _ml/trained_models/my_model_for_search/deployment/_stop

JavaScript Elasticsearch JavaScript Client

const response = await client.ml.stopTrainedModelDeployment({
  model_id: "my_model_for_search",
});

Python Elasticsearch Python Client

resp = client.ml.stop_trained_model_deployment(
    model_id="my_model_for_search",
)

curl Shell

curl -X POST "$ELASTICSEARCH_URL/_ml/trained_models/my_model_for_search/deployment/_stop" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY"

Java Elasticsearch Java Client

client.ml().stopTrainedModelDeployment(s -> s
    .modelId("my_model_for_search")
);

C# Elasticsearch .NET Client

var response = await client.MachineLearning.StopTrainedModelDeploymentAsync(modelId: "my_model_for_search");

PHP Elasticsearch PHP Client

$resp = $client->ml()->stopTrainedModelDeployment([
    "model_id" => "my_model_for_search",
]);

Ruby Elasticsearch Ruby Client

response = client.ml.stop_trained_model_deployment(
  model_id: "my_model_for_search"
)

Response

No example