Knowledge distillation offers a practical way to compress the intelligence of large ensemble models into a single lightweight student model suitable for production deployment. By training on the ensemble's soft probability outputs rather than hard labels, the student inherits nuanced predictive patterns while remaining fast and cost-efficient.
