PATCH /v1/services/mlmodelServices/{id}

from metadata.sdk import configure
from metadata.sdk.entities import MlModelServices

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

# Retrieve, modify, and update
svc = MlModelServices.retrieve("ca22d46e-81b9-4e48-85b5-0adc44980da9")
svc.description = "Updated: Production MLflow tracking server with model registry"
svc.connection = {
    "config": {
        "type": "Mlflow",
        "trackingUri": "http://mlflow-prod:8088",
        "registryUri": "http://mlflow-prod:8088",
        "supportsMetadataExtraction": True
    }
}
updated = MlModelServices.update(svc)

print(f"Updated to version {updated.version}")

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "description": "Updated: Production MLflow tracking server with model registry",
  "version": 0.2,
  "updatedAt": 1769982621618,
  "updatedBy": "admin",
  "href": "http://localhost:8585/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "connection": {
    "config": {
      "type": "Mlflow",
      "trackingUri": "http://mlflow-prod:8088",
      "registryUri": "http://mlflow-prod:8088",
      "supportsMetadataExtraction": true
    }
  },
  "owners": [],
  "tags": [],
  "deleted": false,
  "domains": []
}

PATCH

https://sandbox.getcollate.io/api

services

mlmodelServices

{id}

PATCH /v1/services/mlmodelServices/{id}

from metadata.sdk import configure
from metadata.sdk.entities import MlModelServices

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

# Retrieve, modify, and update
svc = MlModelServices.retrieve("ca22d46e-81b9-4e48-85b5-0adc44980da9")
svc.description = "Updated: Production MLflow tracking server with model registry"
svc.connection = {
    "config": {
        "type": "Mlflow",
        "trackingUri": "http://mlflow-prod:8088",
        "registryUri": "http://mlflow-prod:8088",
        "supportsMetadataExtraction": True
    }
}
updated = MlModelServices.update(svc)

print(f"Updated to version {updated.version}")

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "description": "Updated: Production MLflow tracking server with model registry",
  "version": 0.2,
  "updatedAt": 1769982621618,
  "updatedBy": "admin",
  "href": "http://localhost:8585/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "connection": {
    "config": {
      "type": "Mlflow",
      "trackingUri": "http://mlflow-prod:8088",
      "registryUri": "http://mlflow-prod:8088",
      "supportsMetadataExtraction": true
    }
  },
  "owners": [],
  "tags": [],
  "deleted": false,
  "domains": []
}

Update an ML Model Service

Update an ML model service’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 service to update.

Update by Name

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

fqn

string

required

Fully qualified name of the ML model service (e.g., mlflow_svc).

Body Parameters

Send a JSON object with the fields to update. Only provided fields are changed.

description

string

Updated description in Markdown format.

displayName

string

Updated display name.

connection

object

Updated connection configuration.

Show properties

config

object

Service-specific connection configuration.

owners

array

Updated list of owner references.

Show properties

string

UUID of the owner entity.

type

string

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

tags

array

Updated classification tags.

Show properties

tagFQN

string

required

Fully qualified name of the tag.

labelType

string

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

state

string

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

domain

string

Updated domain FQN.

PATCH /v1/services/mlmodelServices/{id}

from metadata.sdk import configure
from metadata.sdk.entities import MlModelServices

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

# Retrieve, modify, and update
svc = MlModelServices.retrieve("ca22d46e-81b9-4e48-85b5-0adc44980da9")
svc.description = "Updated: Production MLflow tracking server with model registry"
svc.connection = {
    "config": {
        "type": "Mlflow",
        "trackingUri": "http://mlflow-prod:8088",
        "registryUri": "http://mlflow-prod:8088",
        "supportsMetadataExtraction": True
    }
}
updated = MlModelServices.update(svc)

print(f"Updated to version {updated.version}")

{
  "id": "ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "name": "mlflow_svc",
  "fullyQualifiedName": "mlflow_svc",
  "serviceType": "Mlflow",
  "description": "Updated: Production MLflow tracking server with model registry",
  "version": 0.2,
  "updatedAt": 1769982621618,
  "updatedBy": "admin",
  "href": "http://localhost:8585/api/v1/services/mlmodelServices/ca22d46e-81b9-4e48-85b5-0adc44980da9",
  "connection": {
    "config": {
      "type": "Mlflow",
      "trackingUri": "http://mlflow-prod:8088",
      "registryUri": "http://mlflow-prod:8088",
      "supportsMetadataExtraction": true
    }
  },
  "owners": [],
  "tags": [],
  "deleted": false,
  "domains": []
}

Returns

Returns the updated ML model service object with the new version number.

Response

string

Unique identifier for the ML model service (UUID format).

name

string

ML model service name.

fullyQualifiedName

string

Fully qualified name of the service.

description

string

Updated description.

version

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 service
`404`	`NOT_FOUND`	ML model service with given ID or FQN does not exist
`409`	`CONFLICT`	Concurrent modification detected

Retrieve an ML Model Service

Delete an ML Model Service

⌘I

API Reference

Data Assets

Discovery

Data Contracts

Teams & Users

Data Governance

Data Quality

Update an ML Model Service

Update an ML Model Service

Update by ID

Update by Name

Body Parameters

Returns

Response

Error Handling

API Reference

Data Assets

Discovery

Data Contracts

Teams & Users

Data Governance

Data Quality

​Update an ML Model Service

​Update by ID

​Update by Name

​Body Parameters

​Returns

​Response

​Error Handling

Update an ML Model Service

Update by ID

Update by Name

Body Parameters

Returns

Response

Error Handling