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POST
/
v1
/
services
/
mlmodelServices
POST /v1/services/mlmodelServices
from metadata.sdk import configure
from metadata.sdk.entities import MlModelServices
from metadata.generated.schema.api.services.createMlModelService import CreateMlModelServiceRequest

configure(
    host="https://your-company.getcollate.io/api",
    jwt_token="your-jwt-token"
)

request = CreateMlModelServiceRequest(
    name="mlflow_svc",
    displayName="MLflow Production",
    serviceType="Mlflow",
    description="Production MLflow tracking server",
    connection={
        "config": {
            "type": "Mlflow",
            "trackingUri": "http://localhost:8088",
            "registryUri": "http://localhost:8088",
            "supportsMetadataExtraction": True
        }
    }
)

service = MlModelServices.create(request)
print(f"Created: {service.fullyQualifiedName}")

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "description": "Production MLflow tracking server",
  "version": 0.1,
  "updatedAt": 1769982621618,
  "updatedBy": "admin",
  "href": "http://localhost:8585/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "connection": {
    "config": {
      "type": "Mlflow",
      "trackingUri": "http://localhost:8088",
      "registryUri": "http://localhost:8088",
      "supportsMetadataExtraction": true
    }
  },
  "owners": [],
  "tags": [],
  "deleted": false,
  "domains": []
}

Documentation Index

Fetch the complete documentation index at: https://docs.getcollate.io/llms.txt

Use this file to discover all available pages before exploring further.

​
Create an ML Model Service

Create a new ML model service connection to a platform such as Mlflow, Sklearn, or SageMaker.

​
Body Parameters

​
name
string
required
Name of the ML model service. Must be unique across all ML model services.
​
serviceType
string
required
Type of ML model service (e.g., Mlflow, Sklearn, SageMaker, CustomMlModel).
​
connection
object
required
Connection configuration specific to the service type.
​
displayName
string
Human-readable display name for the ML model service.
​
description
string
Description of the ML model service in Markdown format.
​
owners
array
Array of owner references (users or teams) to assign to the service.
​
domain
string
Fully qualified name of the domain to assign for governance purposes.
​
tags
array
Array of classification tags to apply to the ML model service.
POST /v1/services/mlmodelServices
from metadata.sdk import configure
from metadata.sdk.entities import MlModelServices
from metadata.generated.schema.api.services.createMlModelService import CreateMlModelServiceRequest

configure(
    host="https://your-company.getcollate.io/api",
    jwt_token="your-jwt-token"
)

request = CreateMlModelServiceRequest(
    name="mlflow_svc",
    displayName="MLflow Production",
    serviceType="Mlflow",
    description="Production MLflow tracking server",
    connection={
        "config": {
            "type": "Mlflow",
            "trackingUri": "http://localhost:8088",
            "registryUri": "http://localhost:8088",
            "supportsMetadataExtraction": True
        }
    }
)

service = MlModelServices.create(request)
print(f"Created: {service.fullyQualifiedName}")

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "description": "Production MLflow tracking server",
  "version": 0.1,
  "updatedAt": 1769982621618,
  "updatedBy": "admin",
  "href": "http://localhost:8585/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "connection": {
    "config": {
      "type": "Mlflow",
      "trackingUri": "http://localhost:8088",
      "registryUri": "http://localhost:8088",
      "supportsMetadataExtraction": true
    }
  },
  "owners": [],
  "tags": [],
  "deleted": false,
  "domains": []
}

​
Returns

Returns the created ML model service object with all specified properties and system-generated fields.

​
Response

​
id
string
Unique identifier for the ML model service (UUID format).
​
name
string
ML model service name.
​
fullyQualifiedName
string
Fully qualified name of the service.
​
displayName
string
Human-readable display name.
​
description
string
Description of the ML model service in Markdown format.
​
serviceType
string
Type of ML model service (e.g., Mlflow, Sklearn, SageMaker, CustomMlModel).
​
connection
object
Connection configuration for the service.
​
owners
array
List of owners assigned to the ML model service.
​
domain
string
Fully qualified name of the assigned domain.
​
tags
array
Classification tags applied to the ML model service.
​
version
number
Version number for the entity (starts at 0.1).

​
Create or Update (PUT)

Use PUT /v1/services/mlmodelServices instead of POST to perform an upsert. If an ML model service with the same fullyQualifiedName already exists, it will be updated; otherwise, a new service is created. The request body is the same as POST. 
curl -X PUT "{base_url}/api/v1/services/mlmodelServices" \
  -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/services/mlmodelServices/bulk to create or update multiple ML model services in a single request. The request body is an array of create request objects. 
curl -X PUT "{base_url}/api/v1/services/mlmodelServices/bulk" \
  -H "Authorization: Bearer {access_token}" \
  -H "Content-Type: application/json" \
  -d '[
    { "name": "mlflow_prod", "serviceType": "Mlflow", "connection": { "config": { "type": "Mlflow", "trackingUri": "http://mlflow-prod:8088" } } },
    { "name": "sagemaker_prod", "serviceType": "SageMaker", "connection": { "config": { "type": "SageMaker" } } }
  ]'

​
Error Handling

CodeError TypeDescription
400BAD_REQUESTInvalid request body or missing required fields
401UNAUTHORIZEDInvalid or missing authentication token
403FORBIDDENUser lacks permission to create ML model services
409ENTITY_ALREADY_EXISTSML model service with same name already exists (POST only)