Retrieve an ML Model

Get an ML model by ID or fully qualified name

GET /v1/mlmodels/{id}

GET /v1/mlmodels/{id}

Python

from metadata.sdk import configure
from metadata.sdk.entities import MLModels

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

# Get by ID
model = MLModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2")
print(f"{model.fullyQualifiedName}: {model.algorithm}")

# Get by ID with fields
model = MLModels.retrieve(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    fields=["owners", "tags", "followers", "votes"]
)

# Get by fully qualified name
model = MLModels.retrieve_by_name("mlflow_svc.customer_segmentation")

# Get by name with fields
model = MLModels.retrieve_by_name(
    "mlflow_svc.customer_segmentation",
    fields=["owners", "tags", "domains"]
)

Response

{
  "id": "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
  "name": "customer_segmentation",
  "fullyQualifiedName": "mlflow_svc.customer_segmentation",
  "displayName": "Customer Segmentation Model",
  "algorithm": "KMeans",
  "version": 0.1,
  "updatedAt": 1769982669247,
  "updatedBy": "admin",
  "service": {
    "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
    "type": "mlmodelService",
    "name": "mlflow_svc",
    "fullyQualifiedName": "mlflow_svc",
    "deleted": false
  },
  "serviceType": "Mlflow",
  "href": "http://localhost:8585/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
  "deleted": false,
  "owners": [],
  "tags": [],
  "followers": [],
  "votes": {
    "upVotes": 0,
    "downVotes": 0
  },
  "domains": []
}

Retrieve an ML Model

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

Get by ID

id · string · required

UUID of the ML model to retrieve.

fields · string

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

include · string · default: "non-deleted"

Include all, deleted, or non-deleted entities.

Get by Fully Qualified Name

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

fqn · string · required

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

fields · string

Comma-separated list of fields to include: owners, tags, followers, votes, extension, domains, sourceHash.

include · string · default: "non-deleted"

Include all, deleted, or non-deleted entities.

GET /v1/mlmodels/{id}

Python

from metadata.sdk import configure
from metadata.sdk.entities import MLModels

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

# Get by ID
model = MLModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2")
print(f"{model.fullyQualifiedName}: {model.algorithm}")

# Get by ID with fields
model = MLModels.retrieve(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    fields=["owners", "tags", "followers", "votes"]
)

# Get by fully qualified name
model = MLModels.retrieve_by_name("mlflow_svc.customer_segmentation")

# Get by name with fields
model = MLModels.retrieve_by_name(
    "mlflow_svc.customer_segmentation",
    fields=["owners", "tags", "domains"]
)

Response

{
  "id": "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
  "name": "customer_segmentation",
  "fullyQualifiedName": "mlflow_svc.customer_segmentation",
  "displayName": "Customer Segmentation Model",
  "algorithm": "KMeans",
  "version": 0.1,
  "updatedAt": 1769982669247,
  "updatedBy": "admin",
  "service": {
    "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
    "type": "mlmodelService",
    "name": "mlflow_svc",
    "fullyQualifiedName": "mlflow_svc",
    "deleted": false
  },
  "serviceType": "Mlflow",
  "href": "http://localhost:8585/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
  "deleted": false,
  "owners": [],
  "tags": [],
  "followers": [],
  "votes": {
    "upVotes": 0,
    "downVotes": 0
  },
  "domains": []
}

---

Returns

Returns an ML model object with all requested fields populated.

Response

id · string

Unique identifier for the ML model (UUID format).

name · string

ML model name.

fullyQualifiedName · string

Fully qualified name in format service.modelName.

displayName · string

Human-readable display name.

description · string

Description of the ML model in Markdown format.

algorithm · string

Algorithm used by the ML model.

mlFeatures · array

Features used by the ML model.

mlHyperParameters · array

Hyperparameters used by the ML model.

target · string

Target variable or objective.

service · object

Reference to the parent ML model service.

Show properties

id · string

UUID of the ML model service.

type · string

Type of entity (always mlmodelService).

name · string

Name of the ML model service.

fullyQualifiedName · string

Fully qualified name of the ML model service.

serviceType · string

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

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
404	NOT_FOUND	ML model with given ID or FQN does not exist