What is Ensemble?
In Edge AI, an ensemble is a group of multiple AI models working together to improve prediction accuracy and decision-making. Also called model aggregation, it leverages diverse model outputs to produce more reliable results at the edge.
Why Is It Used?
Ensembles are used to reduce errors and improve robustness in AI predictions, especially in resource-constrained Edge AI devices where reliability is critical.
How Is It Used?
Data from edge sensors is processed by multiple AI models simultaneously. Their outputs are combined using methods like averaging, voting, or weighted scoring to generate a final, more accurate prediction.
Types of Ensemble
- Bagging: Combines multiple models trained on different data subsets.
- Boosting: Sequentially trains models, focusing on prior errors.
- Stacking: Uses outputs of several models as input for a final model.
Benefits of Ensemble
- Enhanced accuracy and reliability.
- Greater robustness against sensor noise or edge environment variability.
- Reduced risk of overfitting with diverse model perspectives.
- Optimized performance for real-time Edge AI applications.