Once your model is trained, you must deploy the model on Abacus.AI platform to generate predictions. You can use the prediction dashboard to generate the predictions from the trained model. In this section the underlying prediction API and all other additional prediction API methods are discussed for the use case in consideration:
Method
getClusterPOST
CopyPOST
Predicts the cluster for given data.
Arguments:
REQUIRED
KEY
TYPE
DESCRIPTION
Yes
deploymentToken
str
The deployment token used to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
Yes
deploymentId
str
A unique string identifier for the deployment created under the project.
Yes
queryData
dict
A dictionary where each 'key' represents a column name and its corresponding 'value' represents the value of that column. For Timeseries Clustering, the 'key' should be ITEM_ID, and its value should represent a unique item ID that needs clustering.
Note: The arguments for the API methods follow camelCase but for Python SDK underscore_case is followed.
Response:
KEY
TYPE
DESCRIPTION
success
Boolean
true if the call succeeded, false if there was an error