Model Distillation Pipeline
An end-to-end workflow for transferring knowledge from a large teacher model to a smaller student model, including data generation, training, evaluation, and deployment.
Why It Matters
Distillation pipelines produce models that run 10x faster at 90% of the quality — critical for deploying AI at scale without breaking the budget.
Example
Using GPT-4 to generate high-quality training examples, training a much smaller model on those examples, evaluating against the teacher's performance, and deploying the student.
Think of it like...
Like a master chef creating a simplified recipe book — the apprentice cannot replicate every nuance, but the documented recipes capture most of the expertise at a fraction of the effort.
Related Terms
Knowledge Distillation
A model compression technique where a smaller 'student' model is trained to mimic the behavior of a larger 'teacher' model. The student learns not just correct answers but the teacher's nuanced probability distributions.
Deployment
The process of making a trained ML model available for use in production applications. Deployment involves packaging the model, setting up serving infrastructure, and establishing monitoring.