Update an ML Model

Update ML model properties using JSON Patch

PATCH /v1/mlmodels/{id}

PATCH /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"
)

# Retrieve, modify, and update
model = MLModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2")
model.description = "Updated: Customer segmentation using KMeans with 5 clusters"
model.mlHyperParameters = [
    {"name": "n_clusters", "value": "7"},
    {"name": "max_iter", "value": "500"}
]
updated = MLModels.update(model)

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

Response

{
  "id": "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
  "name": "customer_segmentation",
  "fullyQualifiedName": "mlflow_svc.customer_segmentation",
  "displayName": "Customer Segmentation Model",
  "description": "Updated: Customer segmentation using KMeans with 5 clusters",
  "algorithm": "KMeans",
  "version": 0.2,
  "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": []
}

Update an ML Model

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

Update by ID

id · string · required

UUID of the ML model to update.

Update by Name

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

fqn · string · required

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

Body Parameters

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

description · string

Updated description in Markdown format.

displayName · string

Updated display name.

algorithm · string

Updated algorithm name.

mlFeatures · array

Updated list of ML features.

Show properties

name · string

Name of the feature.

dataType · string

Data type of the feature (e.g., numerical, categorical).

mlHyperParameters · array

Updated list of hyperparameters.

Show properties

name · string

Name of the hyperparameter.

value · string

Value of the hyperparameter.

target · string

Updated target variable.

owners · array

Updated list of owner references.

Show properties

id · string

UUID of the owner entity.

type · string

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

tags · array

Updated classification tags.

Show properties

tagFQN · string · required

Fully qualified name of the tag.

labelType · string

Type of label (e.g., Manual, Derived, Propagated).

state · string

State of the tag (e.g., Suggested, Confirmed).

domain · string

Updated domain FQN.

extension · object

Updated custom property values.

PATCH /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"
)

# Retrieve, modify, and update
model = MLModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2")
model.description = "Updated: Customer segmentation using KMeans with 5 clusters"
model.mlHyperParameters = [
    {"name": "n_clusters", "value": "7"},
    {"name": "max_iter", "value": "500"}
]
updated = MLModels.update(model)

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

Response

{
  "id": "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
  "name": "customer_segmentation",
  "fullyQualifiedName": "mlflow_svc.customer_segmentation",
  "displayName": "Customer Segmentation Model",
  "description": "Updated: Customer segmentation using KMeans with 5 clusters",
  "algorithm": "KMeans",
  "version": 0.2,
  "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 the updated ML model object with the new version number.

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.

description · string

Updated description.

version · 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
404	NOT_FOUND	ML model with given ID or FQN does not exist
409	CONFLICT	Concurrent modification detected