Reference documentation and code samples for the Retail v2 API enum Model.Types.PeriodicTuningState.
Describes whether periodic tuning is enabled for this model
or not. Periodic tuning is scheduled at most every three months. You can
start a tuning process manually by using the TuneModel
method, which starts a tuning process immediately and resets the quarterly
schedule. Enabling or disabling periodic tuning does not affect any
current tuning processes.
The model cannot be tuned with periodic tuning OR the
TuneModel method. Hide the options in customer UI and
reject any requests through the backend self serve API.
PeriodicTuningDisabled
The model has periodic tuning disabled. Tuning
can be reenabled by calling the EnableModelPeriodicTuning
method or by calling the TuneModel method.
PeriodicTuningEnabled
The model has periodic tuning enabled. Tuning
can be disabled by calling the DisableModelPeriodicTuning
method.
Unspecified
Unspecified default value, should never be explicitly set.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-03-21 UTC."],[[["The latest version of the Retail v2 API for `Model.Types.PeriodicTuningState` is 2.12.0."],["This documentation provides reference for different versions of `Model.Types.PeriodicTuningState`, ranging from version 1.0.0 up to the latest 2.12.0."],["`Model.Types.PeriodicTuningState` is an enum that describes if periodic tuning is enabled for a given model, which occurs at most every three months and can be triggered manually."],["There are four states for `Model.Types.PeriodicTuningState`: `AllTuningDisabled`, `PeriodicTuningDisabled`, `PeriodicTuningEnabled`, and `Unspecified`, each with specific implications for tuning the model."]]],[]]
