ALTER ML CONFIGURATION (SQL)
Synopsis
ALTER ML CONFIGURATION ml-configuration-name
[ PROVIDER provider-name] [ %DESCRIPTION description]
[ USING json-object-string ] [ provider-connection-settings ]
Arguments
ml-configuration-name | The name for the ML configuration being altered. |
PROVIDER provider-name | A string specifying the name of a machine learning provider, where values are:
|
%DESCRIPTION description | Optional — String. A text description for the ML configuration. See details below. |
USING json-object-string | Optional — A JSON string specifying one or more key-value pairs; see details below. |
provider-connection-settings | Any additional settings, required for connection, that vary by the machine learning provider. |
Description
The ALTER ML CONFIGURATION statement alters one, or several, parameters within an ML configuration definition. You can alter:
-
The provider
-
The description
-
The USING clause
ML Configuration Description
%DESCRIPTION accepts a text string enclosed in single quotes, which you can use to provide a description for documenting your configuration. This text can be of any length, and can contain any characters, including blank spaces.
USING
You can specify a default USING clause for your configuration. This clause accepts a JSON string with one or more key-value pairs. When TRAIN MODEL is executed, by default the USING clause of the configuration is used.
ALTER ML CONFIGURATION MyConfiguration USING {"seed": 3}
You must make sure that the parameters you specify are recognized by the provider you select. Failing to do so may result in an error when training.
Required Security Privileges
Calling ALTER ML CONFIGURATION requires %ALTER_ML_CONFIGURATION privileges; otherwise, there is a SQLCODE –99 error (Privilege Violation). To assign %ALTER_ML_CONFIGURATION privileges, use the GRANT command.
Examples
The following SQL query edits an existing configuration named TestAutoML to add a USING clause that the user wants used for every model being trained:
ALTER ML CONFIGURATION TestAutoML USING {"seed": 2}