# Create a trained model

**PUT /_ml/trained_models/{model_id}**

Enable you to supply a trained model that is not created by data frame analytics.

## Required authorization

* Cluster privileges: `manage_ml`



## Servers
- http://api.example.com: http://api.example.com ()


## Authentication methods
- Api key auth


## Parameters

### Path parameters
- **model_id** (string)
  The unique identifier of the trained model.

### Query parameters
- **defer_definition_decompression** (boolean)
  If set to `true` and a `compressed_definition` is provided,
  the request defers definition decompression and skips relevant
  validations.
- **wait_for_completion** (boolean)
  Whether to wait for all child operations (e.g. model download)
  to complete.

### Body: application/json (object)

- **compressed_definition** (string)
  The compressed (GZipped and Base64 encoded) inference definition of the
  model. If compressed_definition is specified, then definition cannot be
  specified.
- **definition** (object)
  The inference definition for the model. If definition is specified, then
  compressed_definition cannot be specified.
- **description** (string)
  A human-readable description of the inference trained model.
- **inference_config** (object)
  The default configuration for inference. This can be either a regression
  or classification configuration. It must match the underlying
  definition.trained_model's target_type. For pre-packaged models such as
  ELSER the config is not required.
- **input** (object)
  The input field names for the model definition.
- **metadata** (object)
  An object map that contains metadata about the model.
- **model_type** (string)
  The model type.

  Supported values include:
    - `tree_ensemble`: The model definition is an ensemble model of decision trees.
    - `lang_ident`: A special type reserved for language identification models.
    - `pytorch`: The stored definition is a PyTorch (specifically a TorchScript) model.
  Currently only NLP models are supported.

- **model_size_bytes** (number)
  The estimated memory usage in bytes to keep the trained model in memory.
  This property is supported only if defer_definition_decompression is true
  or the model definition is not supplied.
- **platform_architecture** (string)
  The platform architecture (if applicable) of the trained mode. If the model
  only works on one platform, because it is heavily optimized for a particular
  processor architecture and OS combination, then this field specifies which.
  The format of the string must match the platform identifiers used by Elasticsearch,
  so one of, `linux-x86_64`, `linux-aarch64`, `darwin-x86_64`, `darwin-aarch64`,
  or `windows-x86_64`. For portable models (those that work independent of processor
  architecture or OS features), leave this field unset.
- **tags** (array[string])
  An array of tags to organize the model.
- **prefix_strings** (object)
  Optional prefix strings applied at inference


## Responses
### 200


#### Body: application/json (object)
- **model_id** (string)
  Identifier for the trained model.
- **model_type** (string)
  The model type

  Supported values include:
    - `tree_ensemble`: The model definition is an ensemble model of decision trees.
    - `lang_ident`: A special type reserved for language identification models.
    - `pytorch`: The stored definition is a PyTorch (specifically a TorchScript) model.
  Currently only NLP models are supported.

- **tags** (array[string])
  A comma delimited string of tags. A trained model can have many tags, or none.
- **version** (string)
  The Elasticsearch version number in which the trained model was created.
- **compressed_definition** (string)

- **created_by** (string)
  Information on the creator of the trained model.
- **create_time** (string | number)
  The time when the trained model was created.
- **default_field_map** (object)
  Any field map described in the inference configuration takes precedence.
- **description** (string)
  The free-text description of the trained model.
- **estimated_heap_memory_usage_bytes** (number)
  The estimated heap usage in bytes to keep the trained model in memory.
- **estimated_operations** (number)
  The estimated number of operations to use the trained model.
- **fully_defined** (boolean)
  True if the full model definition is present.
- **inference_config** (object)
  The default configuration for inference. This can be either a regression, classification, or one of the many NLP focused configurations. It must match the underlying definition.trained_model's target_type. For pre-packaged models such as ELSER the config is not required.
- **input** (object)
  The input field names for the model definition.
- **license_level** (string)
  The license level of the trained model.
- **metadata** (object)
  An object containing metadata about the trained model. For example, models created by data frame analytics contain analysis_config and input objects.
- **model_size_bytes** (number | string)

- **model_package** (object)

- **location** (object)

- **platform_architecture** (string)

- **prefix_strings** (object)



[Powered by Bump.sh](https://bump.sh)