What is ensemble learning under computational constraints?

It refers to designing ensembles that balance performance with memory and latency limits.

In production systems, ensembles must meet computational constraints such as inference latency, memory usage, and throughput. Techniques include model pruning, knowledge distillation, lightweight base learners, and limiting ensemble size. Trade-offs must be made between accuracy and efficiency, especially in real-time applications like recommendation systems.