PATCH /v1/services/mlmodelServices/{id}

from metadata.sdk import configure
from metadata.sdk.entities import MlModelServices

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

# Retrieve, modify, and update
svc = MlModelServices.retrieve("ca22d46e-81b9-4e48-85b5-0adc44980da9")
svc.description = "Updated: Production MLflow tracking server with model registry"
svc.connection = {
    "config": {
        "type": "Mlflow",
        "trackingUri": "http://mlflow-prod:8088",
        "registryUri": "http://mlflow-prod:8088",
        "supportsMetadataExtraction": True
    }
}
updated = MlModelServices.update(svc)

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

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

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

# 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": []
}

PATCH

services

mlmodelServices

{id}

PATCH /v1/services/mlmodelServices/{id}

from metadata.sdk import configure
from metadata.sdk.entities import MlModelServices

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

# Retrieve, modify, and update
svc = MlModelServices.retrieve("ca22d46e-81b9-4e48-85b5-0adc44980da9")
svc.description = "Updated: Production MLflow tracking server with model registry"
svc.connection = {
    "config": {
        "type": "Mlflow",
        "trackingUri": "http://mlflow-prod:8088",
        "registryUri": "http://mlflow-prod:8088",
        "supportsMetadataExtraction": True
    }
}
updated = MlModelServices.update(svc)

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

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

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

# 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

Update an ML model service’s properties using JSON Merge Patch. You can update by ID or by fully qualified name.

Update by ID

string

required

UUID of the ML model service to update.

Update by Name

Use PATCH /v1/services/mlmodelServices/name/{fqn} to update by fully qualified name.

string

required

Fully qualified name of the ML model service (e.g., mlflow_svc).

Body Parameters

Send a JSON object with the fields to update. Only provided fields are changed.

string

Updated description in Markdown format.

string

Updated display name.

object

Updated connection configuration.

Show properties

object

Service-specific connection configuration.

array

Updated list of owner references.

Show properties

string

UUID of the owner entity.

string

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

array

Updated classification tags.

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).

string

Updated domain FQN.

PATCH /v1/services/mlmodelServices/{id}

from metadata.sdk import configure
from metadata.sdk.entities import MlModelServices

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

# Retrieve, modify, and update
svc = MlModelServices.retrieve("ca22d46e-81b9-4e48-85b5-0adc44980da9")
svc.description = "Updated: Production MLflow tracking server with model registry"
svc.connection = {
    "config": {
        "type": "Mlflow",
        "trackingUri": "http://mlflow-prod:8088",
        "registryUri": "http://mlflow-prod:8088",
        "supportsMetadataExtraction": True
    }
}
updated = MlModelServices.update(svc)

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

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

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

# 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 JSON patch or malformed request
`401`	`UNAUTHORIZED`	Invalid or missing authentication token
`403`	`FORBIDDEN`	User lacks permission to update this ML model service
`404`	`NOT_FOUND`	ML model service with given ID or FQN does not exist
`409`	`CONFLICT`	Concurrent modification detected

Retrieve an ML Model Service

ML Model Service Versions

API Reference

Data Assets

AI SDK

SDK

Discovery

Lineage

Data Contracts

Teams And Users

Data Governance

Data Quality

Main Concepts

Update an ML Model Service

Update an ML Model Service

Update by ID

Update by Name

Body Parameters

Returns

Response

Error Handling

​Update an ML Model Service

​Update by ID

​Update by Name

​Body Parameters

​Returns

​Response

​Error Handling

Update an ML Model Service

Update by ID

Update by Name

Body Parameters

Returns

Response

Error Handling