AWS Machine Learning Blog· Giuseppe Zappia·· 6 h ago
Build efficient agents with knowledge distillation on Amazon SageMaker AI
Build efficient agents with knowledge distillation on Amazon SageMaker AI
AI summary
Agentic assistants built on frontier models run a multi-step tool loop, and paying frontier prices on every step is hard to sustain at production scale. This post shows how to use sequence-level knowledge distillation (SeqKD) to transfer a frontier teacher's agentic behavior into a small, lower-cost student on Amazon SageMaker AI.
Selection record
Threshold 60Official, first-handFirst 38Second 34
Not admittedSum of both 72 < twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：知识蒸馏训练Agent的AI技术教程
A model scores each item twice, independently, against one written standard, out of 100. An item is admitted only when the two scores add up to twice the threshold. The threshold is set per source tier.
Source: AWS Machine Learning Blog · aws.amazon.com