  Automated Machine Learning (AutoML) significantly simplifies the deployment
of machine learning models by automating tasks from data preprocessing to model
selection to ensembling. AutoML systems for tabular data often employ post hoc
ensembling, where multiple models are combined to improve predictive accuracy.
This typically results in longer inference times, a major limitation in
practical deployments. Addressing this, we introduce a hardware-aware ensemble
selection approach that integrates inference time into post hoc ensembling. By
leveraging an existing framework for ensemble selection with quality diversity
optimization, our method evaluates ensemble candidates for their predictive
accuracy and hardware efficiency. This dual focus allows for a balanced
consideration of accuracy and operational efficiency. Thus, our approach
enables practitioners to choose from a Pareto front of accurate and efficient
ensembles. Our evaluation using 83 classification datasets shows that our
approach sustains competitive accuracy and can significantly improve ensembles'
operational efficiency. The results of this study provide a foundation for
extending these principles to additional hardware constraints, setting the
stage for the development of more resource-efficient AutoML systems.
