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GET
/
v1
/
mlmodels
/
{id}
/
versions
GET /v1/mlmodels/{id}/versions
from metadata.sdk import configure
from metadata.sdk.entities import MLModels

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

# List all versions
versions = MLModels.get_versions("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2")
for v in versions:
    print(f"Version {v.version}: {v.description}")

# Get a specific version
model_v1 = MLModels.get_specific_version(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    "0.1"
)
print(f"Original algorithm: {model_v1.algorithm}")

{
  "entityType": "mlmodel",
  "versions": [
    "{\"id\":\"6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2\",\"name\":\"customer_segmentation\",\"fullyQualifiedName\":\"mlflow_svc.customer_segmentation\",\"version\":0.2,\"description\":\"Updated: Customer segmentation using KMeans with 5 clusters\",\"algorithm\":\"KMeans\",\"serviceType\":\"Mlflow\"}",
    "{\"id\":\"6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2\",\"name\":\"customer_segmentation\",\"fullyQualifiedName\":\"mlflow_svc.customer_segmentation\",\"version\":0.1,\"algorithm\":\"KMeans\",\"serviceType\":\"Mlflow\"}"
  ]
}

​
ML Model Versions

Every change to an ML model entity creates a new version. Use these endpoints to view the version history and retrieve specific versions.

​
List Versions

​
id
string
required
UUID of the ML model.

​
Get Specific Version

Use GET /v1/mlmodels/{id}/versions/{version} to retrieve a specific version.
​
id
string
required
UUID of the ML model.
​
version
string
required
Version number to retrieve (e.g., 0.2).
GET /v1/mlmodels/{id}/versions
from metadata.sdk import configure
from metadata.sdk.entities import MLModels

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

# List all versions
versions = MLModels.get_versions("6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2")
for v in versions:
    print(f"Version {v.version}: {v.description}")

# Get a specific version
model_v1 = MLModels.get_specific_version(
    "6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2",
    "0.1"
)
print(f"Original algorithm: {model_v1.algorithm}")

{
  "entityType": "mlmodel",
  "versions": [
    "{\"id\":\"6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2\",\"name\":\"customer_segmentation\",\"fullyQualifiedName\":\"mlflow_svc.customer_segmentation\",\"version\":0.2,\"description\":\"Updated: Customer segmentation using KMeans with 5 clusters\",\"algorithm\":\"KMeans\",\"serviceType\":\"Mlflow\"}",
    "{\"id\":\"6b04e1d8-b66d-4f78-ab21-beb5be2cf4f2\",\"name\":\"customer_segmentation\",\"fullyQualifiedName\":\"mlflow_svc.customer_segmentation\",\"version\":0.1,\"algorithm\":\"KMeans\",\"serviceType\":\"Mlflow\"}"
  ]
}

​
Returns

List versions returns an object with entityType and a versions array of serialized entity snapshots (newest first). Get specific version returns the full ML model object as it existed at that version.

​
Error Handling

CodeError TypeDescription
401UNAUTHORIZEDInvalid or missing authentication token
404NOT_FOUNDML model or version does not exist