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POST
/
v1
/
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.getcollate.io/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}")

{
  "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": []
}

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

Create a new ML model within an ML model service.

​
Body Parameters

​
name
string
required
Name of the ML model. Must be unique within the parent ML model service.
​
service
string
required
Fully qualified name of the parent MlModelService (e.g., mlflow_svc).
​
algorithm
string
Algorithm used by the ML model (e.g., KMeans, RandomForest, XGBoost, Neural Network).
​
displayName
string
Human-readable display name for the ML model.
​
description
string
Description of the ML model in Markdown format.
​
mlFeatures
array
Array of ML features used by the model.
​
mlHyperParameters
array
Array of hyperparameters used by the model.
​
target
string
Target variable or objective of the ML model.
​
server
string
Endpoint URL for the model serving server.
​
dashboard
object
Reference to an associated dashboard entity.
​
owners
array
Array of owner references (users or teams) to assign to the ML model.
​
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.
​
extension
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.getcollate.io/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}")

{
  "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

​
id
string
Unique identifier for the ML model (UUID format).
​
name
string
ML model name.
​
fullyQualifiedName
string
Fully qualified name in format service.modelName.
​
displayName
string
Human-readable display name.
​
description
string
Description of the ML model in Markdown format.
​
algorithm
string
Algorithm used by the ML model.
​
mlFeatures
array
Features used by the ML model.
​
mlHyperParameters
array
Hyperparameters used by the ML model.
​
target
string
Target variable or objective of the ML model.
​
service
object
Reference to the parent ML model service.
​
serviceType
string
Type of ML model service (e.g., Mlflow, Sklearn, SageMaker).
​
owners
array
List of owners assigned to the ML model.
​
domains
array
Domain assignments for governance.
​
tags
array
Classification tags applied to the ML model.
​
extension
object
Custom property values defined by your organization’s metadata schema.
​
version
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

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