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GET
https://sandbox.getcollate.io/api
/
v1
/
mlmodels
/
{id}
GET /v1/mlmodels/{id}
from metadata.sdk import configure
from metadata.sdk.entities import MLModels

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

# Get by ID
model = MLModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2")
print(f"{model.fullyQualifiedName}: {model.algorithm}")

# Get by ID with fields
model = MLModels.retrieve(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    fields=["owners", "tags", "followers", "votes"]
)

# Get by fully qualified name
model = MLModels.retrieve_by_name("mlflow_svc.customer_segmentation")

# Get by name with fields
model = MLModels.retrieve_by_name(
    "mlflow_svc.customer_segmentation",
    fields=["owners", "tags", "domains"]
)

{
  "id": "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
  "name": "customer_segmentation",
  "fullyQualifiedName": "mlflow_svc.customer_segmentation",
  "displayName": "Customer Segmentation Model",
  "algorithm": "KMeans",
  "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": []
}

​
Retrieve an ML Model

Get a single ML model by its unique ID or fully qualified name.

​
Get by ID

​
id
string
required
UUID of the ML model to retrieve.
​
fields
string
Comma-separated list of fields to include (e.g., owners,tags,followers,votes,extension,domains,sourceHash).
​
include
string
default:"non-deleted"
Include all, deleted, or non-deleted entities.

​
Get by Fully Qualified Name

Use GET /v1/mlmodels/name/{fqn} to retrieve by fully qualified name.
​
fqn
string
required
Fully qualified name of the ML model (e.g., mlflow_svc.customer_segmentation).
​
fields
string
Comma-separated list of fields to include: owners, tags, followers, votes, extension, domains, sourceHash.
​
include
string
default:"non-deleted"
Include all, deleted, or non-deleted entities.
GET /v1/mlmodels/{id}
from metadata.sdk import configure
from metadata.sdk.entities import MLModels

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

# Get by ID
model = MLModels.retrieve("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2")
print(f"{model.fullyQualifiedName}: {model.algorithm}")

# Get by ID with fields
model = MLModels.retrieve(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    fields=["owners", "tags", "followers", "votes"]
)

# Get by fully qualified name
model = MLModels.retrieve_by_name("mlflow_svc.customer_segmentation")

# Get by name with fields
model = MLModels.retrieve_by_name(
    "mlflow_svc.customer_segmentation",
    fields=["owners", "tags", "domains"]
)

{
  "id": "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
  "name": "customer_segmentation",
  "fullyQualifiedName": "mlflow_svc.customer_segmentation",
  "displayName": "Customer Segmentation Model",
  "algorithm": "KMeans",
  "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 an ML model object with all requested fields populated.

​
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.
​
service
object
Reference to the parent ML model service.
​
serviceType
string
Type of ML model service (e.g., Mlflow, Sklearn, SageMaker).
​
version
number
Version number for the entity.
​
owners
array
List of owners. Only included when fields contains owners.
​
tags
array
Classification tags. Only included when fields contains tags.
​
domains
array
Domain assignments. Only included when fields contains domains.

​
Error Handling

CodeError TypeDescription
401UNAUTHORIZEDInvalid or missing authentication token
403FORBIDDENUser lacks permission to view this ML model
404NOT_FOUNDML model with given ID or FQN does not exist