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.root}")

import org.openmetadata.schema.entity.data.MlModel;

// client is an initialized OpenMetadataClient.

// Retrieve, modify, and update
MlModel model = client.mlModels().get("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");
model.setDescription("Updated: Customer segmentation using KMeans with 5 clusters");
MlModel updated = client.mlModels().update(model.getId(), 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.root}")

import org.openmetadata.schema.entity.data.MlModel;

// client is an initialized OpenMetadataClient.

// Retrieve, modify, and update
MlModel model = client.mlModels().get("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2");
model.setDescription("Updated: Customer segmentation using KMeans with 5 clusters");
MlModel updated = client.mlModels().update(model.getId(), 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, for example 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, for example 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, for example user, team.

array

Updated classification tags.

Show properties

string

required

Fully qualified name of the tag.

string

Type of label, for example Manual, Derived, Propagated.

string

State of the tag, for example Suggested, Confirmed.

array

Updated domains, as fully qualified names.

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.root}")

import org.openmetadata.schema.entity.data.MlModel;

// client is an initialized OpenMetadataClient.

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

API Reference

SDK

API

Data Assets

AI Assets

Discovery

Lineage

Data Contracts

Teams & Users

Data Governance

Data Quality

Alerts

Incidents

Billing

Query Runner

Context Plugins

Standards

Update an ML Model

Update an ML Model

Update by ID

Update by Name

Body Parameters

Returns

Response

Error Handling