POST /v1/mlmodels

from metadata.sdk import configure
from metadata.sdk.entities import MLModels
from metadata.generated.schema.api.data.createMlModel import CreateMlModelRequest

configure(
    host="https://your-company.open-metadata.org/api",
    jwt_token="your-jwt-token"
)

request = CreateMlModelRequest(
    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"
)

model = MLModels.create(request)
print(f"Created: {model.fullyQualifiedName.root}")

import java.util.List;
import org.openmetadata.schema.api.data.CreateMlModel;
import org.openmetadata.schema.entity.data.MlModel;
import org.openmetadata.schema.type.MlFeature;
import org.openmetadata.schema.type.MlFeatureDataType;
import org.openmetadata.sdk.fluent.MlModels;

CreateMlModel request = new CreateMlModel()
    .withName("customer_segmentation")
    .withDisplayName("Customer Segmentation Model")
    .withService("mlflow_svc")
    .withAlgorithm("KMeans")
    .withDescription("Segments customers into groups based on purchasing behavior")
    .withMlFeatures(List.of(
        new MlFeature().withName("total_spend").withDataType(MlFeatureDataType.Numerical),
        new MlFeature().withName("purchase_frequency").withDataType(MlFeatureDataType.Numerical)));

MlModel model = MlModels.create(request);

curl -X POST "{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"
  }'

{
  "id": "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
  "name": "customer_segmentation",
  "fullyQualifiedName": "mlflow_svc.customer_segmentation",
  "displayName": "Customer Segmentation Model",
  "description": "Segments customers into groups based on purchasing behavior",
  "algorithm": "KMeans",
  "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",
  "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": []
}

POST

mlmodels

POST /v1/mlmodels

from metadata.sdk import configure
from metadata.sdk.entities import MLModels
from metadata.generated.schema.api.data.createMlModel import CreateMlModelRequest

configure(
    host="https://your-company.open-metadata.org/api",
    jwt_token="your-jwt-token"
)

request = CreateMlModelRequest(
    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"
)

model = MLModels.create(request)
print(f"Created: {model.fullyQualifiedName.root}")

import java.util.List;
import org.openmetadata.schema.api.data.CreateMlModel;
import org.openmetadata.schema.entity.data.MlModel;
import org.openmetadata.schema.type.MlFeature;
import org.openmetadata.schema.type.MlFeatureDataType;
import org.openmetadata.sdk.fluent.MlModels;

CreateMlModel request = new CreateMlModel()
    .withName("customer_segmentation")
    .withDisplayName("Customer Segmentation Model")
    .withService("mlflow_svc")
    .withAlgorithm("KMeans")
    .withDescription("Segments customers into groups based on purchasing behavior")
    .withMlFeatures(List.of(
        new MlFeature().withName("total_spend").withDataType(MlFeatureDataType.Numerical),
        new MlFeature().withName("purchase_frequency").withDataType(MlFeatureDataType.Numerical)));

MlModel model = MlModels.create(request);

curl -X POST "{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"
  }'

{
  "id": "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
  "name": "customer_segmentation",
  "fullyQualifiedName": "mlflow_svc.customer_segmentation",
  "displayName": "Customer Segmentation Model",
  "description": "Segments customers into groups based on purchasing behavior",
  "algorithm": "KMeans",
  "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",
  "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": []
}

Create an ML Model

Create a new ML model within an ML model service.

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.

object

Reference to an associated dashboard entity.

Show properties

string

UUID of the dashboard entity.

string

Type of entity (always 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.

POST /v1/mlmodels

from metadata.sdk import configure
from metadata.sdk.entities import MLModels
from metadata.generated.schema.api.data.createMlModel import CreateMlModelRequest

configure(
    host="https://your-company.open-metadata.org/api",
    jwt_token="your-jwt-token"
)

request = CreateMlModelRequest(
    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"
)

model = MLModels.create(request)
print(f"Created: {model.fullyQualifiedName.root}")

import java.util.List;
import org.openmetadata.schema.api.data.CreateMlModel;
import org.openmetadata.schema.entity.data.MlModel;
import org.openmetadata.schema.type.MlFeature;
import org.openmetadata.schema.type.MlFeatureDataType;
import org.openmetadata.sdk.fluent.MlModels;

CreateMlModel request = new CreateMlModel()
    .withName("customer_segmentation")
    .withDisplayName("Customer Segmentation Model")
    .withService("mlflow_svc")
    .withAlgorithm("KMeans")
    .withDescription("Segments customers into groups based on purchasing behavior")
    .withMlFeatures(List.of(
        new MlFeature().withName("total_spend").withDataType(MlFeatureDataType.Numerical),
        new MlFeature().withName("purchase_frequency").withDataType(MlFeatureDataType.Numerical)));

MlModel model = MlModels.create(request);

curl -X POST "{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"
  }'

{
  "id": "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
  "name": "customer_segmentation",
  "fullyQualifiedName": "mlflow_svc.customer_segmentation",
  "displayName": "Customer Segmentation Model",
  "description": "Segments customers into groups based on purchasing behavior",
  "algorithm": "KMeans",
  "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",
  "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 the created ML model object with all specified properties and system-generated fields.

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.

Show properties

string

Name of the feature.

string

Data type of the feature.

array

Sources of the feature data.

array

Hyperparameters used by the ML model.

Show properties

string

Name of the hyperparameter.

string

Value of the hyperparameter.

string

Target variable or objective of the ML model.

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.

array

List of owners assigned 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

Domain assignments for governance.

array

Classification tags applied to the ML model.

Show properties

string

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.

number

Version number for the entity (starts at 0.1).

Create or Update (PUT)

Use PUT /v1/mlmodels instead of POST to perform an upsert. If an ML model with the same fullyQualifiedName already exists, it will be updated; otherwise, a new ML model is created. The request body is the same as POST.

curl -X PUT "{base_url}/api/v1/mlmodels" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '{ ... same body as POST ... }'

PUT will not return a 409 conflict error if the entity already exists — it will update the existing entity instead.

Bulk Create or Update (PUT)

Use PUT /v1/mlmodels/bulk to create or update multiple ML models in a single request. The request body is an array of create request objects.

curl -X PUT "{base_url}/api/v1/mlmodels/bulk" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '[
    { "name": "model_one", "service": "mlflow_svc", "algorithm": "KMeans" },
    { "name": "model_two", "service": "mlflow_svc", "algorithm": "RandomForest" }
  ]'

Error Handling

Code	Error Type	Description
`400`	`BAD_REQUEST`	Invalid request body or missing required fields
`401`	`UNAUTHORIZED`	Invalid or missing authentication token
`403`	`FORBIDDEN`	User lacks permission to create ML models
`409`	`ENTITY_ALREADY_EXISTS`	ML model with same name already exists in service (POST only)

API Reference

Data Assets

Collaboration

AI SDK

SDK

Discovery

Lineage

Data Contracts

Teams And Users

Data Governance

Data Quality

Alerts

Incidents

Main Concepts

Create an ML Model

Create an ML Model

Body Parameters

Returns

Response

Create or Update (PUT)

Bulk Create or Update (PUT)

Error Handling