> ## 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.

# Create an ML Model

> Create a new ML model within an ML model service

# Create an ML Model

Create a new ML model within an ML model service.

## Body Parameters

<ParamField body="name" type="string" required>
  Name of the ML model. Must be unique within the parent ML model service.
</ParamField>

<ParamField body="service" type="string" required>
  Fully qualified name of the parent MlModelService (e.g., `mlflow_svc`).
</ParamField>

<ParamField body="algorithm" type="string">
  Algorithm used by the ML model (e.g., `KMeans`, `RandomForest`, `XGBoost`, `Neural Network`).
</ParamField>

<ParamField body="displayName" type="string">
  Human-readable display name for the ML model.
</ParamField>

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

<ParamField body="mlFeatures" type="array">
  Array of ML features used by the model.

  <Expandable title="properties">
    <ParamField body="name" type="string">
      Name of the feature.
    </ParamField>

    <ParamField body="dataType" type="string">
      Data type of the feature (e.g., `numerical`, `categorical`).
    </ParamField>

    <ParamField body="featureSources" type="array">
      Sources of the feature data.

      <Expandable title="properties">
        <ParamField body="name" type="string">
          Name of the feature source.
        </ParamField>

        <ParamField body="dataType" type="string">
          Data type of the source.
        </ParamField>

        <ParamField body="dataSource" type="object">
          Reference to the source entity (e.g., a table or column).
        </ParamField>
      </Expandable>
    </ParamField>
  </Expandable>
</ParamField>

<ParamField body="mlHyperParameters" type="array">
  Array of hyperparameters used by the model.

  <Expandable title="properties">
    <ParamField body="name" type="string">
      Name of the hyperparameter.
    </ParamField>

    <ParamField body="value" type="string">
      Value of the hyperparameter.
    </ParamField>
  </Expandable>
</ParamField>

<ParamField body="target" type="string">
  Target variable or objective of the ML model.
</ParamField>

<ParamField body="server" type="string">
  Endpoint URL for the model serving server.
</ParamField>

<ParamField body="dashboard" type="object">
  Reference to an associated dashboard entity.

  <Expandable title="properties">
    <ParamField body="id" type="string">
      UUID of the dashboard entity.
    </ParamField>

    <ParamField body="type" type="string">
      Type of entity (always `dashboard`).
    </ParamField>
  </Expandable>
</ParamField>

<ParamField body="owners" type="array">
  Array of owner references (users or teams) to assign to the ML model.

  <Expandable title="properties">
    <ParamField body="id" type="string">
      UUID of the owner entity.
    </ParamField>

    <ParamField body="type" type="string">
      Type of owner entity (e.g., `user`, `team`).
    </ParamField>

    <ParamField body="name" type="string">
      Name of the owner entity.
    </ParamField>
  </Expandable>
</ParamField>

<ParamField body="domain" type="string">
  Fully qualified name of the domain to assign for governance purposes.
</ParamField>

<ParamField body="tags" type="array">
  Array of classification tags to apply to the ML model.

  <Expandable title="properties">
    <ParamField body="tagFQN" type="string" required>
      Fully qualified name of the tag.
    </ParamField>

    <ParamField body="labelType" type="string">
      Type of label (e.g., `Manual`, `Derived`, `Propagated`).
    </ParamField>

    <ParamField body="state" type="string">
      State of the tag (e.g., `Suggested`, `Confirmed`).
    </ParamField>
  </Expandable>
</ParamField>

<ParamField body="extension" type="object">
  Custom property values defined by your organization's metadata schema.
</ParamField>

<RequestExample dropdown>
  ```python POST /v1/mlmodels theme={null}
  from metadata.sdk import configure
  from metadata.sdk.entities import MLModels
  from metadata.generated.schema.api.data.createMlModel import CreateMlModelRequest

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

  request = CreateMlModelRequest(
      name="customer_segmentation",
      displayName="Customer Segmentation Model",
      service="mlflow_svc",
      algorithm="KMeans",
      description="Segments customers into groups based on purchasing behavior",
      mlFeatures=[
          {
              "name": "total_spend",
              "dataType": "numerical"
          },
          {
              "name": "purchase_frequency",
              "dataType": "numerical"
          }
      ],
      mlHyperParameters=[
          {"name": "n_clusters", "value": "5"},
          {"name": "max_iter", "value": "300"}
      ],
      target="customer_segment"
  )

  model = MLModels.create(request)
  print(f"Created: {model.fullyQualifiedName}")
  ```

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

  // Create using builder pattern
  var model = MlModels.builder()
      .name("customer_segmentation")
      .displayName("Customer Segmentation Model")
      .service("mlflow_svc")
      .algorithm("KMeans")
      .description("Segments customers into groups based on purchasing behavior")
      .create();
  ```

  ```bash POST /v1/mlmodels theme={null}
  curl -X POST "{base_url}/api/v1/mlmodels" \
    -H "Authorization: Bearer {access_token}" \
    -H "Content-Type: application/json" \
    -d '{
      "name": "customer_segmentation",
      "displayName": "Customer Segmentation Model",
      "service": "mlflow_svc",
      "algorithm": "KMeans",
      "description": "Segments customers into groups based on purchasing behavior",
      "mlFeatures": [
        {
          "name": "total_spend",
          "dataType": "numerical"
        },
        {
          "name": "purchase_frequency",
          "dataType": "numerical"
        }
      ],
      "mlHyperParameters": [
        {"name": "n_clusters", "value": "5"},
        {"name": "max_iter", "value": "300"}
      ],
      "target": "customer_segment"
    }'
  ```
</RequestExample>

<ResponseExample>
  ```json Response theme={null}
  {
    "id": "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    "name": "customer_segmentation",
    "fullyQualifiedName": "mlflow_svc.customer_segmentation",
    "displayName": "Customer Segmentation Model",
    "description": "Segments customers into groups based on purchasing behavior",
    "algorithm": "KMeans",
    "mlFeatures": [
      {
        "name": "total_spend",
        "dataType": "numerical"
      },
      {
        "name": "purchase_frequency",
        "dataType": "numerical"
      }
    ],
    "mlHyperParameters": [
      {"name": "n_clusters", "value": "5"},
      {"name": "max_iter", "value": "300"}
    ],
    "target": "customer_segment",
    "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 the created ML model object with all specified properties and system-generated fields.

## 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">
  Features used by the ML model.

  <Expandable title="properties">
    <ResponseField name="name" type="string">
      Name of the feature.
    </ResponseField>

    <ResponseField name="dataType" type="string">
      Data type of the feature.
    </ResponseField>

    <ResponseField name="featureSources" type="array">
      Sources of the feature data.
    </ResponseField>
  </Expandable>
</ResponseField>

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

  <Expandable title="properties">
    <ResponseField name="name" type="string">
      Name of the hyperparameter.
    </ResponseField>

    <ResponseField name="value" type="string">
      Value of the hyperparameter.
    </ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="target" type="string">
  Target variable or objective of the ML model.
</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="owners" type="array" optional>
  List of owners assigned to the ML model.

  <Expandable title="properties">
    <ResponseField name="id" type="string">
      UUID of the owner entity.
    </ResponseField>

    <ResponseField name="type" type="string">
      Type of owner entity (e.g., `user`, `team`).
    </ResponseField>

    <ResponseField name="name" type="string">
      Name of the owner entity.
    </ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="domains" type="array" optional>
  Domain assignments for governance.
</ResponseField>

<ResponseField name="tags" type="array" optional>
  Classification tags applied to the ML model.

  <Expandable title="properties">
    <ResponseField name="tagFQN" type="string">
      Fully qualified name of the tag.
    </ResponseField>

    <ResponseField name="labelType" type="string">
      Type of label (e.g., `Manual`, `Derived`, `Propagated`).
    </ResponseField>

    <ResponseField name="state" type="string">
      State of the tag (e.g., `Suggested`, `Confirmed`).
    </ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="extension" type="object" optional>
  Custom property values defined by your organization's metadata schema.
</ResponseField>

<ResponseField name="version" type="number">
  Version number for the entity (starts at 0.1).
</ResponseField>

***

## Create or Update (PUT)

Use `PUT /v1/mlmodels` instead of `POST` to perform an upsert. If an ML model with the same `fullyQualifiedName` already exists, it will be updated; otherwise, a new ML model is created. The request body is the same as `POST`.

```bash theme={null}
curl -X PUT "{base_url}/api/v1/mlmodels" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{ ... same body as POST ... }'
```

<Note>
  `PUT` will not return a `409` conflict error if the entity already exists -- it will update the existing entity instead.
</Note>

***

## Bulk Create or Update (PUT)

Use `PUT /v1/mlmodels/bulk` to create or update multiple ML models in a single request. The request body is an array of create request objects.

```bash theme={null}
curl -X PUT "{base_url}/api/v1/mlmodels/bulk" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '[
    { "name": "model_one", "service": "mlflow_svc", "algorithm": "KMeans" },
    { "name": "model_two", "service": "mlflow_svc", "algorithm": "RandomForest" }
  ]'
```

***

## Error Handling

| Code  | Error Type              | Description                                                   |
| ----- | ----------------------- | ------------------------------------------------------------- |
| `400` | `BAD_REQUEST`           | Invalid request body or missing required fields               |
| `401` | `UNAUTHORIZED`          | Invalid or missing authentication token                       |
| `403` | `FORBIDDEN`             | User lacks permission to create ML models                     |
| `409` | `ENTITY_ALREADY_EXISTS` | ML model with same name already exists in service (POST only) |