GET /v1/mlmodels/{id}

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"]
)

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

// Get by ID
var model = MlModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");

// Get by ID with fields
var model = MlModels.retrieve(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    "owners,tags,followers,votes"
);

// Get by fully qualified name
var model = MlModels.retrieveByName("mlflow_svc.customer_segmentation");

// Get by name with fields
var model = MlModels.retrieveByName(
    "mlflow_svc.customer_segmentation",
    "owners,tags,domains"
);

# Get by ID
curl "{base_url}/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2" \
  -H "Authorization: Bearer {access_token}"

# Get by ID with fields
curl "{base_url}/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2?fields=owners,tags,domains" \
  -H "Authorization: Bearer {access_token}"

# Get by fully qualified name
curl "{base_url}/api/v1/mlmodels/name/mlflow_svc.customer_segmentation" \
  -H "Authorization: Bearer {access_token}"

# Get by name with fields
curl "{base_url}/api/v1/mlmodels/name/mlflow_svc.customer_segmentation?fields=owners,tags,domains" \
  -H "Authorization: Bearer {access_token}"

{
  "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": []
}

GET

mlmodels

{id}

GET /v1/mlmodels/{id}

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"]
)

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

// Get by ID
var model = MlModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");

// Get by ID with fields
var model = MlModels.retrieve(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    "owners,tags,followers,votes"
);

// Get by fully qualified name
var model = MlModels.retrieveByName("mlflow_svc.customer_segmentation");

// Get by name with fields
var model = MlModels.retrieveByName(
    "mlflow_svc.customer_segmentation",
    "owners,tags,domains"
);

# Get by ID
curl "{base_url}/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2" \
  -H "Authorization: Bearer {access_token}"

# Get by ID with fields
curl "{base_url}/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2?fields=owners,tags,domains" \
  -H "Authorization: Bearer {access_token}"

# Get by fully qualified name
curl "{base_url}/api/v1/mlmodels/name/mlflow_svc.customer_segmentation" \
  -H "Authorization: Bearer {access_token}"

# Get by name with fields
curl "{base_url}/api/v1/mlmodels/name/mlflow_svc.customer_segmentation?fields=owners,tags,domains" \
  -H "Authorization: Bearer {access_token}"

{
  "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

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}

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"]
)

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

// Get by ID
var model = MlModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");

// Get by ID with fields
var model = MlModels.retrieve(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    "owners,tags,followers,votes"
);

// Get by fully qualified name
var model = MlModels.retrieveByName("mlflow_svc.customer_segmentation");

// Get by name with fields
var model = MlModels.retrieveByName(
    "mlflow_svc.customer_segmentation",
    "owners,tags,domains"
);

# Get by ID
curl "{base_url}/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2" \
  -H "Authorization: Bearer {access_token}"

# Get by ID with fields
curl "{base_url}/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2?fields=owners,tags,domains" \
  -H "Authorization: Bearer {access_token}"

# Get by fully qualified name
curl "{base_url}/api/v1/mlmodels/name/mlflow_svc.customer_segmentation" \
  -H "Authorization: Bearer {access_token}"

# Get by name with fields
curl "{base_url}/api/v1/mlmodels/name/mlflow_svc.customer_segmentation?fields=owners,tags,domains" \
  -H "Authorization: Bearer {access_token}"

{
  "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

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

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.

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

List ML Models

Update an ML Model

​Retrieve an ML Model

​Get by ID

​Get by Fully Qualified Name

​Returns

​Response

​Error Handling

Retrieve an ML Model

Get by ID

Get by Fully Qualified Name

Returns

Response

Error Handling