Generating a selectable suggestion using a provisional machine learning model when use of a default suggestion model is inconsequential

Abstract

Implementations set forth herein relate to selectively relying on additional suggestion model(s) when generating selectable suggestions, while also maintaining access to a default suggestion model. The selectable suggestions can be generated using one or more additional multi-domain machine learning (ML) models, which can be optionally available to the client application, regardless of whether a default suggestion model remains useful for generating suitable suggestions. In some implementations, as the client application employs various additional multi-domain ML models, a particular model can be identified as improving suggestions for the client application, at least based on user feedback and/or other data. The particular model can then be selected to replace and/or supplement the default suggestion model, in order to provide more accurate suggestions that, when selected, initialize actions that can preserve time and computational resources.