AWS Machine Learning Blog · Giuseppe Zappia · 3 小时前模型评分59
在 Amazon SageMaker 人工智能（AI，Artificial Intelligence） 上用知识蒸馏构建高效智能体
Build efficient agents with knowledge distillation on Amazon SageMaker AI
导读
AWS 团队在 Amazon SageMaker 人工智能（AI，Artificial Intelligence） 上演示用序列级知识蒸馏（SeqKD，Sequence-Level Knowledge Distillation）把购物智能体从 GLM-5.3 教师蒸馏到 Qwen3.5-4B 学生模型。
分类置信度
高
- 官方或准官方来源，且无冲突类别。
- 正文、可信日期、主分类和证据摘录完整。
来源级别：官方一手
应用领域：零售与营销
事件类型：分析与教程
证据摘录
- “In this post you will learn how to use knowledge distillation, and specifically sequence-level knowledge distillation (SeqKD), to fine-tune a shopping agent on Amazon SageMaker AI .”
- “A real-time inference endpoint bills for as long as it runs, so tear down the endpoint that you stood up for serving after you have your results.”
- “The training job itself needs no cleanup.”
- “The one thing that does keep costing you is your data in S3 (the encoded trajectories and output artifacts), which incurs storage cost for as long as you keep it, so delete what you no longer need or move it to a lower-cost storage class.”
来源：AWS Machine Learning Blog · aws.amazon.com