POST /v1/services/mlmodelServices

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}")

import static org.openmetadata.sdk.fluent.MlModelServices.*;

MlModelService service = MlModelServices.builder()
    .name("mlflow_svc")
    .serviceType("Mlflow")
    .connection(mlflowConnection()
        .trackingUri("http://localhost:8088")
        .registryUri("http://localhost:8088"))
    .create();

curl -X POST "{base_url}/api/v1/services/mlmodelServices" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "mlflow_svc",
    "serviceType": "Mlflow",
    "description": "Production MLflow tracking server",
    "connection": {
      "config": {
        "type": "Mlflow",
        "trackingUri": "http://localhost:8088",
        "registryUri": "http://localhost:8088",
        "supportsMetadataExtraction": true
      }
    }
  }'

{
  "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": []
}

POST

services

mlmodelServices

POST /v1/services/mlmodelServices

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}")

import static org.openmetadata.sdk.fluent.MlModelServices.*;

MlModelService service = MlModelServices.builder()
    .name("mlflow_svc")
    .serviceType("Mlflow")
    .connection(mlflowConnection()
        .trackingUri("http://localhost:8088")
        .registryUri("http://localhost:8088"))
    .create();

curl -X POST "{base_url}/api/v1/services/mlmodelServices" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "mlflow_svc",
    "serviceType": "Mlflow",
    "description": "Production MLflow tracking server",
    "connection": {
      "config": {
        "type": "Mlflow",
        "trackingUri": "http://localhost:8088",
        "registryUri": "http://localhost:8088",
        "supportsMetadataExtraction": true
      }
    }
  }'

{
  "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

string

required

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

string

required

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

object

required

Connection configuration specific to the service type.

Show properties

object

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

string

Human-readable display name for the ML model service.

string

Description of the ML model service in Markdown format.

array

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

Show properties

string

UUID of the owner entity.

string

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

string

Name of the owner entity.

string

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

array

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

Show properties

string

required

Fully qualified name of the tag.

string

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

string

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

POST /v1/services/mlmodelServices

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}")

import static org.openmetadata.sdk.fluent.MlModelServices.*;

MlModelService service = MlModelServices.builder()
    .name("mlflow_svc")
    .serviceType("Mlflow")
    .connection(mlflowConnection()
        .trackingUri("http://localhost:8088")
        .registryUri("http://localhost:8088"))
    .create();

curl -X POST "{base_url}/api/v1/services/mlmodelServices" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "mlflow_svc",
    "serviceType": "Mlflow",
    "description": "Production MLflow tracking server",
    "connection": {
      "config": {
        "type": "Mlflow",
        "trackingUri": "http://localhost:8088",
        "registryUri": "http://localhost:8088",
        "supportsMetadataExtraction": true
      }
    }
  }'

{
  "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

string

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

string

ML model service name.

string

Fully qualified name of the service.

string

Human-readable display name.

string

Description of the ML model service in Markdown format.

string

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

object

Connection configuration for the service.

Show properties

object

Service-specific connection configuration.

array

List of owners assigned to the ML model service.

Show properties

string

UUID of the owner entity.

string

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

string

Name of the owner entity.

string

Fully qualified name of the assigned domain.

array

Classification tags applied to the ML model service.

Show properties

string

Fully qualified name of the tag.

string

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

string

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

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)

API Reference

Data Assets

AI SDK

SDK

Discovery

Lineage

Data Contracts

Teams And Users

Data Governance

Data Quality

Main Concepts

Create an ML Model Service

Create an ML Model Service

Body Parameters

Returns

Response

Create or Update (PUT)

Bulk Create or Update (PUT)

Error Handling

​Create an ML Model Service

​Body Parameters

​Returns

​Response

​Create or Update (PUT)

​Bulk Create or Update (PUT)

​Error Handling

Create an ML Model Service

Body Parameters

Returns

Response

Create or Update (PUT)

Bulk Create or Update (PUT)

Error Handling