PATCH /v1/mlmodels/{id}

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

configure(
    host="https://your-company.getcollate.io/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}")

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

// Retrieve, modify, and update
var model = MlModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");
model.setDescription("Updated: Customer segmentation using KMeans with 5 clusters");
var updated = MlModels.update(model);

# Update by ID
curl -X PATCH "{base_url}/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Customer segmentation using KMeans with 5 clusters"}
  ]'

# Update by name
curl -X PATCH "{base_url}/api/v1/mlmodels/name/mlflow_svc.customer_segmentation" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Customer segmentation using KMeans with 5 clusters"}
  ]'

{
  "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": []
}

PATCH

mlmodels

{id}

PATCH /v1/mlmodels/{id}

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

configure(
    host="https://your-company.getcollate.io/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}")

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

// Retrieve, modify, and update
var model = MlModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");
model.setDescription("Updated: Customer segmentation using KMeans with 5 clusters");
var updated = MlModels.update(model);

# Update by ID
curl -X PATCH "{base_url}/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Customer segmentation using KMeans with 5 clusters"}
  ]'

# Update by name
curl -X PATCH "{base_url}/api/v1/mlmodels/name/mlflow_svc.customer_segmentation" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Customer segmentation using KMeans with 5 clusters"}
  ]'

{
  "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

string

required

UUID of the ML model to update.

Update by Name

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

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.

string

Updated description in Markdown format.

string

Updated display name.

string

Updated algorithm name.

array

Updated list of ML features.

Show properties

string

Name of the feature.

string

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

array

Updated list of hyperparameters.

Show properties

string

Name of the hyperparameter.

string

Value of the hyperparameter.

string

Updated target variable.

array

Updated list of owner references.

Show properties

string

UUID of the owner entity.

string

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

array

Updated classification tags.

Show properties

string

required

Fully qualified name of the tag.

string

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

string

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

string

Updated domain FQN.

object

Updated custom property values.

PATCH /v1/mlmodels/{id}

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

configure(
    host="https://your-company.getcollate.io/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}")

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

// Retrieve, modify, and update
var model = MlModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");
model.setDescription("Updated: Customer segmentation using KMeans with 5 clusters");
var updated = MlModels.update(model);

# Update by ID
curl -X PATCH "{base_url}/api/v1/mlmodels/6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Customer segmentation using KMeans with 5 clusters"}
  ]'

# Update by name
curl -X PATCH "{base_url}/api/v1/mlmodels/name/mlflow_svc.customer_segmentation" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json-patch+json" \
  -d '[
    {"op": "replace", "path": "/description", "value": "Updated: Customer segmentation using KMeans with 5 clusters"}
  ]'

{
  "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

string

Unique identifier for the ML model (UUID format).

string

ML model name.

string

Fully qualified name in format service.modelName.

string

Updated description.

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

Retrieve an ML Model

Delete an ML Model

⌘I

​Update an ML Model

​Update by ID

​Update by Name

​Body Parameters

​Returns

​Response

​Error Handling

Update an ML Model

Update by ID

Update by Name

Body Parameters

Returns

Response

Error Handling