PUT /v1/services/mlmodelServices

curl -X PUT "{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
      }
    }
  }'

from metadata.generated.schema.entity.services.connections.metadata.openMetadataConnection import (
    AuthProvider,
    OpenMetadataConnection,
)
from metadata.generated.schema.security.client.openMetadataJWTClientConfig import (
    OpenMetadataJWTClientConfig,
)
from metadata.ingestion.ometa.ometa_api import OpenMetadata
from metadata.generated.schema.entity.services.mlmodelService import MlModelService

metadata = OpenMetadata(
    OpenMetadataConnection(
        hostPort="https://your-company.getcollate.io/api",
        authProvider=AuthProvider.openmetadata,
        securityConfig=OpenMetadataJWTClientConfig(jwtToken="your-jwt-token"),
    )
)

# Retrieve, modify, and update
svc = metadata.get_by_id(entity=MlModelService, entity_id="ca22d46e-81b9-4e48-85b5-0adc44980da9")
modified = svc.model_copy(deep=True)
modified.description = "Updated: Production MLflow tracking server with model registry"
modified.connection.config.trackingUri = "http://mlflow-prod:8088"
modified.connection.config.registryUri = "http://mlflow-prod:8088"
updated = metadata.patch(entity=MlModelService, source=svc, destination=modified)

print(f"Updated to version {updated.version.root}")

import org.openmetadata.schema.entity.services.MlModelService;
import org.openmetadata.sdk.fluent.MlModelServices;

// client is an initialized OpenMetadataClient.
MlModelServices.setDefaultClient(client);

// Retrieve, modify, and update
MlModelService service = MlModelServices.retrieve("ca22d46e-81b9-4e48-85b5-0adc44980da9");
service.setDescription("Updated: Production MLflow tracking server with model registry");
MlModelService updated = MlModelServices.update(service);

# Update by ID
curl -X PATCH "{base_url}/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Production MLflow tracking server with model registry"}
  ]'

# Update by name
curl -X PATCH "{base_url}/api/v1/services/mlmodelServices/name/mlflow_svc" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Production MLflow tracking server with model registry"}
  ]'

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "description": "Updated: Production MLflow tracking server with model registry",
  "version": 0.2,
  "updatedAt": 1769982621618,
  "updatedBy": "admin",
  "href": "http://localhost:8585/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "connection": {
    "config": {
      "type": "Mlflow",
      "trackingUri": "http://mlflow-prod:8088",
      "registryUri": "http://mlflow-prod:8088",
      "supportsMetadataExtraction": true
    }
  },
  "owners": [],
  "tags": [],
  "deleted": false,
  "domains": []
}

PUT

services

mlmodelServices

PUT /v1/services/mlmodelServices

curl -X PUT "{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
      }
    }
  }'

from metadata.generated.schema.entity.services.connections.metadata.openMetadataConnection import (
    AuthProvider,
    OpenMetadataConnection,
)
from metadata.generated.schema.security.client.openMetadataJWTClientConfig import (
    OpenMetadataJWTClientConfig,
)
from metadata.ingestion.ometa.ometa_api import OpenMetadata
from metadata.generated.schema.entity.services.mlmodelService import MlModelService

metadata = OpenMetadata(
    OpenMetadataConnection(
        hostPort="https://your-company.getcollate.io/api",
        authProvider=AuthProvider.openmetadata,
        securityConfig=OpenMetadataJWTClientConfig(jwtToken="your-jwt-token"),
    )
)

# Retrieve, modify, and update
svc = metadata.get_by_id(entity=MlModelService, entity_id="ca22d46e-81b9-4e48-85b5-0adc44980da9")
modified = svc.model_copy(deep=True)
modified.description = "Updated: Production MLflow tracking server with model registry"
modified.connection.config.trackingUri = "http://mlflow-prod:8088"
modified.connection.config.registryUri = "http://mlflow-prod:8088"
updated = metadata.patch(entity=MlModelService, source=svc, destination=modified)

print(f"Updated to version {updated.version.root}")

import org.openmetadata.schema.entity.services.MlModelService;
import org.openmetadata.sdk.fluent.MlModelServices;

// client is an initialized OpenMetadataClient.
MlModelServices.setDefaultClient(client);

// Retrieve, modify, and update
MlModelService service = MlModelServices.retrieve("ca22d46e-81b9-4e48-85b5-0adc44980da9");
service.setDescription("Updated: Production MLflow tracking server with model registry");
MlModelService updated = MlModelServices.update(service);

# Update by ID
curl -X PATCH "{base_url}/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Production MLflow tracking server with model registry"}
  ]'

# Update by name
curl -X PATCH "{base_url}/api/v1/services/mlmodelServices/name/mlflow_svc" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Production MLflow tracking server with model registry"}
  ]'

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "description": "Updated: Production MLflow tracking server with model registry",
  "version": 0.2,
  "updatedAt": 1769982621618,
  "updatedBy": "admin",
  "href": "http://localhost:8585/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "connection": {
    "config": {
      "type": "Mlflow",
      "trackingUri": "http://mlflow-prod:8088",
      "registryUri": "http://mlflow-prod:8088",
      "supportsMetadataExtraction": true
    }
  },
  "owners": [],
  "tags": [],
  "deleted": false,
  "domains": []
}

Update an ML Model Service

Create an ML model service or update it if it already exists, using PUT /v1/services/mlmodelServices with the full request body. Send the complete definition, not just the fields you want to change. To change only some fields, use PATCH with a JSON Patch array and the application/json-patch+json content type, as shown in the PATCH examples below.

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 (for example, Mlflow, Sklearn, SageMaker, CustomMlModel).

object

required

Connection configuration specific to the service type.

Show properties

object

Service-specific connection configuration (for example, 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, for example user, team.

string

Name of the owner entity.

array

Array of fully qualified names of the domains 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, for example Manual, Derived, Propagated.

string

State of the tag, for example Suggested, Confirmed.

PUT /v1/services/mlmodelServices

curl -X PUT "{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
      }
    }
  }'

from metadata.generated.schema.entity.services.connections.metadata.openMetadataConnection import (
    AuthProvider,
    OpenMetadataConnection,
)
from metadata.generated.schema.security.client.openMetadataJWTClientConfig import (
    OpenMetadataJWTClientConfig,
)
from metadata.ingestion.ometa.ometa_api import OpenMetadata
from metadata.generated.schema.entity.services.mlmodelService import MlModelService

metadata = OpenMetadata(
    OpenMetadataConnection(
        hostPort="https://your-company.getcollate.io/api",
        authProvider=AuthProvider.openmetadata,
        securityConfig=OpenMetadataJWTClientConfig(jwtToken="your-jwt-token"),
    )
)

# Retrieve, modify, and update
svc = metadata.get_by_id(entity=MlModelService, entity_id="ca22d46e-81b9-4e48-85b5-0adc44980da9")
modified = svc.model_copy(deep=True)
modified.description = "Updated: Production MLflow tracking server with model registry"
modified.connection.config.trackingUri = "http://mlflow-prod:8088"
modified.connection.config.registryUri = "http://mlflow-prod:8088"
updated = metadata.patch(entity=MlModelService, source=svc, destination=modified)

print(f"Updated to version {updated.version.root}")

import org.openmetadata.schema.entity.services.MlModelService;
import org.openmetadata.sdk.fluent.MlModelServices;

// client is an initialized OpenMetadataClient.
MlModelServices.setDefaultClient(client);

// Retrieve, modify, and update
MlModelService service = MlModelServices.retrieve("ca22d46e-81b9-4e48-85b5-0adc44980da9");
service.setDescription("Updated: Production MLflow tracking server with model registry");
MlModelService updated = MlModelServices.update(service);

# Update by ID
curl -X PATCH "{base_url}/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Production MLflow tracking server with model registry"}
  ]'

# Update by name
curl -X PATCH "{base_url}/api/v1/services/mlmodelServices/name/mlflow_svc" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Production MLflow tracking server with model registry"}
  ]'

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "description": "Updated: Production MLflow tracking server with model registry",
  "version": 0.2,
  "updatedAt": 1769982621618,
  "updatedBy": "admin",
  "href": "http://localhost:8585/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "connection": {
    "config": {
      "type": "Mlflow",
      "trackingUri": "http://mlflow-prod:8088",
      "registryUri": "http://mlflow-prod:8088",
      "supportsMetadataExtraction": true
    }
  },
  "owners": [],
  "tags": [],
  "deleted": false,
  "domains": []
}

Returns

Returns the updated ML model service object with the new version number.

Response

string

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

string

ML model service name.

string

Fully qualified name of the service.

string

Updated description.

number

Incremented version number.

Error Handling

Code	Error Type	Description
`400`	`BAD_REQUEST`	Invalid or malformed request body
`401`	`UNAUTHORIZED`	Invalid or missing authentication token
`403`	`FORBIDDEN`	User lacks permission to update this ML model service
`409`	`CONFLICT`	Concurrent modification detected

​Update an ML Model Service

​Body Parameters

​Returns

​Response

​Error Handling

Update an ML Model Service

Body Parameters

Returns

Response

Error Handling