What is model distillation in production ML pipelines?

Model distillation compresses a large teacher model into a smaller student model for efficient inference.

Knowledge distillation transfers learned representations from a large, high-performing teacher model into a smaller student model. This reduces latency and memory usage while maintaining acceptable accuracy. In MLOps, distillation is used for edge deployment, cost reduction, and scaling inference systems.