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.root}: {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 org.openmetadata.schema.entity.data.MlModel;

// client is an initialized OpenMetadataClient.

// Get by ID
MlModel model = client.mlModels().get("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");

// Get by ID with fields
MlModel modelWithFields = client.mlModels().get(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    "owners,tags,followers,votes"
);

// Get by fully qualified name
MlModel modelByName = client.mlModels().getByName("mlflow_svc.customer_segmentation");

// Get by name with fields
MlModel modelByNameWithFields = client.mlModels().getByName(
    "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.root}: {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 org.openmetadata.schema.entity.data.MlModel;

// client is an initialized OpenMetadataClient.

// Get by ID
MlModel model = client.mlModels().get("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");

// Get by ID with fields
MlModel modelWithFields = client.mlModels().get(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    "owners,tags,followers,votes"
);

// Get by fully qualified name
MlModel modelByName = client.mlModels().getByName("mlflow_svc.customer_segmentation");

// Get by name with fields
MlModel modelByNameWithFields = client.mlModels().getByName(
    "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.

string

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

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.

string

required

Fully qualified name of the ML model, for example mlflow_svc.customer_segmentation.

string

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

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.root}: {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 org.openmetadata.schema.entity.data.MlModel;

// client is an initialized OpenMetadataClient.

// Get by ID
MlModel model = client.mlModels().get("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");

// Get by ID with fields
MlModel modelWithFields = client.mlModels().get(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    "owners,tags,followers,votes"
);

// Get by fully qualified name
MlModel modelByName = client.mlModels().getByName("mlflow_svc.customer_segmentation");

// Get by name with fields
MlModel modelByNameWithFields = client.mlModels().getByName(
    "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).

string

ML model name.

string

Fully qualified name in format service.modelName.

string

Human-readable display name.

string

Description of the ML model in Markdown format.

string

Algorithm used by the ML model.

array

Features used by the ML model.

array

Hyperparameters used by the ML model.

string

Target variable or objective.

object

Reference to the parent ML model service.

Show properties

string

UUID of the ML model service.

string

Type of entity (always mlmodelService).

string

Name of the ML model service.

string

Fully qualified name of the ML model service.

string

Type of ML model service, for example Mlflow, Sklearn, SageMaker.

number

Version number for the entity.

array

List of owners. Only included when fields contains owners.

array

Classification tags. Only included when fields contains tags.

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

API Reference

Data Assets

AI Assets

AI SDK

SDK

Discovery

Lineage

Data Contracts

Teams And Users

Data Governance

Data Quality

Alerts

Incidents

Main Concepts

Retrieve an ML Model

Retrieve an ML Model

Get by ID

Get by Fully Qualified Name

Returns

Response

Error Handling