> ## Documentation Index
> Fetch the complete documentation index at: https://docs.open-metadata.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Retrieve an ML Model

> Get an ML model by ID or fully qualified name

# Retrieve an ML Model

Get a single ML model by its unique ID or fully qualified name.

## Get by ID

<ParamField path="id" type="string" required>
  UUID of the ML model to retrieve.
</ParamField>

<ParamField query="fields" type="string">
  Comma-separated list of fields to include (e.g., `owners,tags,followers,votes,extension,domains,sourceHash`).
</ParamField>

<ParamField query="include" type="string" default="non-deleted">
  Include `all`, `deleted`, or `non-deleted` entities.
</ParamField>

## Get by Fully Qualified Name

Use `GET /v1/mlmodels/name/{fqn}` to retrieve by fully qualified name.

<ParamField path="fqn" type="string" required>
  Fully qualified name of the ML model (e.g., `mlflow_svc.customer_segmentation`).
</ParamField>

<ParamField query="fields" type="string">
  Comma-separated list of fields to include: `owners`, `tags`, `followers`, `votes`, `extension`, `domains`, `sourceHash`.
</ParamField>

<ParamField query="include" type="string" default="non-deleted">
  Include `all`, `deleted`, or `non-deleted` entities.
</ParamField>

<RequestExample dropdown>
  ```python GET /v1/mlmodels/{id} theme={null}
  from metadata.sdk import configure
  from metadata.sdk.entities import MLModels

  configure(
      host="https://your-company.open-metadata.org/api",
      jwt_token="your-jwt-token"
  )

  # Get by ID
  model = MLModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2")
  print(f"{model.fullyQualifiedName}: {model.algorithm}")

  # Get by ID with fields
  model = MLModels.retrieve(
      "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
      fields=["owners", "tags", "followers", "votes"]
  )

  # Get by fully qualified name
  model = MLModels.retrieve_by_name("mlflow_svc.customer_segmentation")

  # Get by name with fields
  model = MLModels.retrieve_by_name(
      "mlflow_svc.customer_segmentation",
      fields=["owners", "tags", "domains"]
  )
  ```

  ```java GET /v1/mlmodels/{id} theme={null}
  import static org.openmetadata.sdk.fluent.MlModels.*;

  // Get by ID
  var model = MlModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");

  // Get by ID with fields
  var model = MlModels.retrieve(
      "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
      "owners,tags,followers,votes"
  );

  // Get by fully qualified name
  var model = MlModels.retrieveByName("mlflow_svc.customer_segmentation");

  // Get by name with fields
  var model = MlModels.retrieveByName(
      "mlflow_svc.customer_segmentation",
      "owners,tags,domains"
  );
  ```

  ```bash GET /v1/mlmodels/{id} theme={null}
  # Get by ID
  curl "{base_url}/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2" \
    -H "Authorization: Bearer {access_token}"

  # Get by ID with fields
  curl "{base_url}/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2?fields=owners,tags,domains" \
    -H "Authorization: Bearer {access_token}"

  # Get by fully qualified name
  curl "{base_url}/api/v1/mlmodels/name/mlflow_svc.customer_segmentation" \
    -H "Authorization: Bearer {access_token}"

  # Get by name with fields
  curl "{base_url}/api/v1/mlmodels/name/mlflow_svc.customer_segmentation?fields=owners,tags,domains" \
    -H "Authorization: Bearer {access_token}"
  ```
</RequestExample>

<ResponseExample>
  ```json Response theme={null}
  {
    "id": "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    "name": "customer_segmentation",
    "fullyQualifiedName": "mlflow_svc.customer_segmentation",
    "displayName": "Customer Segmentation Model",
    "algorithm": "KMeans",
    "version": 0.1,
    "updatedAt": 1769982669247,
    "updatedBy": "admin",
    "service": {
      "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
      "type": "mlmodelService",
      "name": "mlflow_svc",
      "fullyQualifiedName": "mlflow_svc",
      "deleted": false
    },
    "serviceType": "Mlflow",
    "href": "http://localhost:8585/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    "deleted": false,
    "owners": [],
    "tags": [],
    "followers": [],
    "votes": {
      "upVotes": 0,
      "downVotes": 0
    },
    "domains": []
  }
  ```
</ResponseExample>

***

## Returns

Returns an ML model object with all requested fields populated.

## Response

<ResponseField name="id" type="string">
  Unique identifier for the ML model (UUID format).
</ResponseField>

<ResponseField name="name" type="string">
  ML model name.
</ResponseField>

<ResponseField name="fullyQualifiedName" type="string">
  Fully qualified name in format `service.modelName`.
</ResponseField>

<ResponseField name="displayName" type="string">
  Human-readable display name.
</ResponseField>

<ResponseField name="description" type="string">
  Description of the ML model in Markdown format.
</ResponseField>

<ResponseField name="algorithm" type="string">
  Algorithm used by the ML model.
</ResponseField>

<ResponseField name="mlFeatures" type="array" optional>
  Features used by the ML model.
</ResponseField>

<ResponseField name="mlHyperParameters" type="array" optional>
  Hyperparameters used by the ML model.
</ResponseField>

<ResponseField name="target" type="string" optional>
  Target variable or objective.
</ResponseField>

<ResponseField name="service" type="object">
  Reference to the parent ML model service.

  <Expandable title="properties">
    <ResponseField name="id" type="string">
      UUID of the ML model service.
    </ResponseField>

    <ResponseField name="type" type="string">
      Type of entity (always `mlmodelService`).
    </ResponseField>

    <ResponseField name="name" type="string">
      Name of the ML model service.
    </ResponseField>

    <ResponseField name="fullyQualifiedName" type="string">
      Fully qualified name of the ML model service.
    </ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="serviceType" type="string">
  Type of ML model service (e.g., Mlflow, Sklearn, SageMaker).
</ResponseField>

<ResponseField name="version" type="number">
  Version number for the entity.
</ResponseField>

<ResponseField name="owners" type="array" optional>
  List of owners. Only included when `fields` contains `owners`.
</ResponseField>

<ResponseField name="tags" type="array" optional>
  Classification tags. Only included when `fields` contains `tags`.
</ResponseField>

<ResponseField name="domains" type="array" optional>
  Domain assignments. Only included when `fields` contains `domains`.
</ResponseField>

***

## Error Handling

| Code  | Error Type     | Description                                  |
| ----- | -------------- | -------------------------------------------- |
| `401` | `UNAUTHORIZED` | Invalid or missing authentication token      |
| `403` | `FORBIDDEN`    | User lacks permission to view this ML model  |
| `404` | `NOT_FOUND`    | ML model with given ID or FQN does not exist |