Create an ML Model Service

Create a new ML model service connection

POST /v1/services/mlmodelServices

POST /v1/services/mlmodelServices

Python

from metadata.sdk import configure
from metadata.sdk.entities import MlModelServices
from metadata.generated.schema.api.services.createMlModelService import CreateMlModelServiceRequest

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

request = CreateMlModelServiceRequest(
    name="mlflow_svc",
    displayName="MLflow Production",
    serviceType="Mlflow",
    description="Production MLflow tracking server",
    connection={
        "config": {
            "type": "Mlflow",
            "trackingUri": "http://localhost:8088",
            "registryUri": "http://localhost:8088",
            "supportsMetadataExtraction": True
        }
    }
)

service = MlModelServices.create(request)
print(f"Created: {service.fullyQualifiedName}")

Response

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "description": "Production MLflow tracking server",
  "version": 0.1,
  "updatedAt": 1769982621618,
  "updatedBy": "admin",
  "href": "http://localhost:8585/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "connection": {
    "config": {
      "type": "Mlflow",
      "trackingUri": "http://localhost:8088",
      "registryUri": "http://localhost:8088",
      "supportsMetadataExtraction": true
    }
  },
  "owners": [],
  "tags": [],
  "deleted": false,
  "domains": []
}

Create an ML Model Service

Create a new ML model service connection to a platform such as Mlflow, Sklearn, or SageMaker.

Body Parameters

name · string · required

Name of the ML model service. Must be unique across all ML model services.

serviceType · string · required

Type of ML model service (e.g., Mlflow, Sklearn, SageMaker, CustomMlModel).

connection · object · required

Connection configuration specific to the service type.

Show properties

config · object

Service-specific connection configuration (e.g., trackingUri, registryUri for Mlflow).

displayName · string

Human-readable display name for the ML model service.

description · string

Description of the ML model service in Markdown format.

owners · array

Array of owner references (users or teams) to assign to the service.

Show properties

id · string

UUID of the owner entity.

type · string

Type of owner entity (e.g., user, team).

name · string

Name of the owner entity.

domain · string

Fully qualified name of the domain to assign for governance purposes.

tags · array

Array of classification tags to apply to the ML model service.

Show properties

tagFQN · string · required

Fully qualified name of the tag.

labelType · string

Type of label (e.g., Manual, Derived, Propagated).

state · string

State of the tag (e.g., Suggested, Confirmed).

POST /v1/services/mlmodelServices

Python

from metadata.sdk import configure
from metadata.sdk.entities import MlModelServices
from metadata.generated.schema.api.services.createMlModelService import CreateMlModelServiceRequest

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

request = CreateMlModelServiceRequest(
    name="mlflow_svc",
    displayName="MLflow Production",
    serviceType="Mlflow",
    description="Production MLflow tracking server",
    connection={
        "config": {
            "type": "Mlflow",
            "trackingUri": "http://localhost:8088",
            "registryUri": "http://localhost:8088",
            "supportsMetadataExtraction": True
        }
    }
)

service = MlModelServices.create(request)
print(f"Created: {service.fullyQualifiedName}")

Response

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "description": "Production MLflow tracking server",
  "version": 0.1,
  "updatedAt": 1769982621618,
  "updatedBy": "admin",
  "href": "http://localhost:8585/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "connection": {
    "config": {
      "type": "Mlflow",
      "trackingUri": "http://localhost:8088",
      "registryUri": "http://localhost:8088",
      "supportsMetadataExtraction": true
    }
  },
  "owners": [],
  "tags": [],
  "deleted": false,
  "domains": []
}

---

Returns

Returns the created ML model service object with all specified properties and system-generated fields.

Response

id · string

Unique identifier for the ML model service (UUID format).

name · string

ML model service name.

fullyQualifiedName · string

Fully qualified name of the service.

displayName · string

Human-readable display name.

description · string

Description of the ML model service in Markdown format.

serviceType · string

Type of ML model service (e.g., Mlflow, Sklearn, SageMaker, CustomMlModel).

connection · object

Connection configuration for the service.

Show properties

config · object

Service-specific connection configuration.

owners · array

List of owners assigned to the ML model service.

Show properties

id · string

UUID of the owner entity.

type · string

Type of owner entity (e.g., user, team).

name · string

Name of the owner entity.

domain · string

Fully qualified name of the assigned domain.

tags · array

Classification tags applied to the ML model service.

Show properties

tagFQN · string

Fully qualified name of the tag.

labelType · string

Type of label (e.g., Manual, Derived, Propagated).

state · string

State of the tag (e.g., Suggested, Confirmed).

version · number

Version number for the entity (starts at 0.1).

---

Create or Update (PUT)

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

curl -X PUT "{base_url}/api/v1/services/mlmodelServices" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{ ... same body as POST ... }'

PUT will not return a 409 conflict error if the entity already exists — it will update the existing entity instead.

---

Bulk Create or Update (PUT)

Use PUT /v1/services/mlmodelServices/bulk to create or update multiple ML model services in a single request. The request body is an array of create request objects.

curl -X PUT "{base_url}/api/v1/services/mlmodelServices/bulk" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '[
    { "name": "mlflow_prod", "serviceType": "Mlflow", "connection": { "config": { "type": "Mlflow", "trackingUri": "http://mlflow-prod:8088" } } },
    { "name": "sagemaker_prod", "serviceType": "SageMaker", "connection": { "config": { "type": "SageMaker" } } }
  ]'

---

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 model services
409	ENTITY_ALREADY_EXISTS	ML model service with same name already exists (POST only)