Retrieve an ML Model Service

Get an ML model service by ID or fully qualified name

GET /v1/services/mlmodelServices/{id}

GET /v1/services/mlmodelServices/{id}

Python

from metadata.sdk import configure, client as sdk_client

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

# Get by ID
svc = client.get("/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9")
print(f"{svc['fullyQualifiedName']}: {svc['serviceType']}")

# Get by ID with fields
svc = client.get(
    "/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
    data={"fields": "owners,tags"},
)

# Get by fully qualified name
svc = client.get("/services/mlmodelServices/name/mlflow_svc")

# Get by name with fields
svc = client.get(
    "/services/mlmodelServices/name/mlflow_svc",
    data={"fields": "owners,tags,domains"},
)

Response

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "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": []
}

Retrieve an ML Model Service

Get a single ML model service by its unique ID or fully qualified name.

Get by ID

id · string · required

UUID of the ML model service to retrieve.

fields · string

Comma-separated list of fields to include (e.g., owners,tags,domains).

include · string · default: "non-deleted"

Include all, deleted, or non-deleted entities.

Get by Fully Qualified Name

Use GET /v1/services/mlmodelServices/name/{fqn} to retrieve by fully qualified name.

fqn · string · required

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

fields · string

Comma-separated list of fields to include: owners, tags, domains.

include · string · default: "non-deleted"

Include all, deleted, or non-deleted entities.

GET /v1/services/mlmodelServices/{id}

Python

from metadata.sdk import configure, client as sdk_client

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

# Get by ID
svc = client.get("/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9")
print(f"{svc['fullyQualifiedName']}: {svc['serviceType']}")

# Get by ID with fields
svc = client.get(
    "/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
    data={"fields": "owners,tags"},
)

# Get by fully qualified name
svc = client.get("/services/mlmodelServices/name/mlflow_svc")

# Get by name with fields
svc = client.get(
    "/services/mlmodelServices/name/mlflow_svc",
    data={"fields": "owners,tags,domains"},
)

Response

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "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 an ML model service object with all requested fields populated.

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.

version · number

Version number for the entity.

owners · array

List of owners. Only included when fields contains owners.

tags · array

Classification tags. Only included when fields contains tags.

domains · array

Domain assignments. Only included when fields contains domains.

---

Error Handling

Code	Error Type	Description
401	UNAUTHORIZED	Invalid or missing authentication token
403	FORBIDDEN	User lacks permission to view this ML model service
404	NOT_FOUND	ML model service with given ID or FQN does not exist