How to Integrate Knowledge Distillation into Your MLOps Pipeline

Knowledge distillation transfers capabilities from a large teacher model to a compact student model, drastically reducing size and power consumption for inference. Integrating this into MLOps transforms it from a one-off experiment into a repeatable, automated production process. This involves automating teacher model selection, versioning student checkpoints with tools like MLflow or Weights & Biases, and establishing a CI/CD pipeline that triggers retraining on new data. The goal is to make model compression a core, governed component of your AI lifecycle.

A production pipeline must handle the entire student model lifecycle: automated training, validation against efficiency KPIs, and deployment alongside existing serving infrastructure like KServe or Seldon Core. You'll set up continuous evaluation to monitor for performance drift and efficiency regressions, ensuring the distilled model remains optimal. This guide provides the actionable steps to build this system, connecting to our pillar on Knowledge Distillation and Model Pruning for Sustainability and related guides on benchmarking performance and continuous evaluation systems.