PUT /v1/mlmodels

curl -X PUT "{base_url}/api/v1/mlmodels" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "customer_segmentation",
    "displayName": "Customer Segmentation Model",
    "service": "mlflow_svc",
    "algorithm": "KMeans",
    "description": "Segments customers into groups based on purchasing behavior",
    "mlFeatures": [
      {
        "name": "total_spend",
        "dataType": "numerical"
      },
      {
        "name": "purchase_frequency",
        "dataType": "numerical"
      }
    ],
    "mlHyperParameters": [
      {"name": "n_clusters", "value": "5"},
      {"name": "max_iter", "value": "300"}
    ],
    "target": "customer_segment"
  }'

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.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": []
}

PUT

mlmodels

PUT /v1/mlmodels

curl -X PUT "{base_url}/api/v1/mlmodels" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "customer_segmentation",
    "displayName": "Customer Segmentation Model",
    "service": "mlflow_svc",
    "algorithm": "KMeans",
    "description": "Segments customers into groups based on purchasing behavior",
    "mlFeatures": [
      {
        "name": "total_spend",
        "dataType": "numerical"
      },
      {
        "name": "purchase_frequency",
        "dataType": "numerical"
      }
    ],
    "mlHyperParameters": [
      {"name": "n_clusters", "value": "5"},
      {"name": "max_iter", "value": "300"}
    ],
    "target": "customer_segment"
  }'

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.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

Create an ML model or update it if it already exists, using PUT /v1/mlmodels with the full request body. Send the complete definition, not just the fields you want to change. To change only some fields, use PATCH with a JSON Patch array and the application/json-patch+json content type, as shown in the PATCH examples below.

Body Parameters

string

required

Name of the ML model. Must be unique within the parent ML model service.

string

required

Fully qualified name of the parent MlModelService, for example mlflow_svc.

string

Algorithm used by the ML model, for example KMeans, RandomForest, XGBoost, Neural Network.

string

Human-readable display name for the ML model.

string

Description of the ML model in Markdown format.

array

Array of ML features used by the model.

Show properties

string

Name of the feature.

string

Data type of the feature, for example numerical, categorical.

array

Sources of the feature data.

Show properties

string

Name of the feature source.

string

Data type of the source.

object

Reference to the source entity, for example a table or column.

array

Array of hyperparameters used by the model.

Show properties

string

Name of the hyperparameter.

string

Value of the hyperparameter.

string

Target variable or objective of the ML model.

string

Endpoint URL for the model serving server.

string

Fully qualified name of the associated dashboard, for example sample_looker.model_dashboard.

array

Array of owner references (users or teams) to assign to the ML model.

Show properties

string

UUID of the owner entity.

string

Type of owner entity, for example user, team.

string

Name of the owner entity.

array

Fully qualified names of the domains to assign for governance purposes.

array

Array of classification tags to apply to the ML model.

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.

object

Custom property values defined by your organization’s metadata schema.

PUT /v1/mlmodels

curl -X PUT "{base_url}/api/v1/mlmodels" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "customer_segmentation",
    "displayName": "Customer Segmentation Model",
    "service": "mlflow_svc",
    "algorithm": "KMeans",
    "description": "Segments customers into groups based on purchasing behavior",
    "mlFeatures": [
      {
        "name": "total_spend",
        "dataType": "numerical"
      },
      {
        "name": "purchase_frequency",
        "dataType": "numerical"
      }
    ],
    "mlHyperParameters": [
      {"name": "n_clusters", "value": "5"},
      {"name": "max_iter", "value": "300"}
    ],
    "target": "customer_segment"
  }'

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.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 or malformed request body
`401`	`UNAUTHORIZED`	Invalid or missing authentication token
`403`	`FORBIDDEN`	User lacks permission to update this ML model
`409`	`CONFLICT`	Concurrent modification detected

API Reference

Data Assets

Collaboration

AI SDK

SDK

Discovery

Lineage

Data Contracts

Teams And Users

Data Governance

Data Quality

Alerts

Incidents

Main Concepts

Update an ML Model

Update an ML Model

Body Parameters

Returns

Response

Error Handling

​Update an ML Model

​Body Parameters

​Returns

​Response

​Error Handling

Update an ML Model

Body Parameters

Returns

Response

Error Handling