用 SageMaker 详细指标和 CloudWatch Insights 监控生成式 AI 推理
Monitor and debug generative AI inference with SageMaker detailed metrics and Insights dashboard on CloudWatch
AWS 教你用 CloudWatch 盯着 SageMaker 上的生成式 AI 推理，有详细指标和仪表盘，调性能抓问题都好使。
Amazon SageMaker AI 提供全托管实时推理，支持单模型端点（SME）和推理组件（IC）两种架构。
通过 CloudWatch 详细指标和 Insights 仪表盘，用户可监控生成式 AI 推理的延迟、吞吐量等关键指标。该仪表盘支持自定义视图和异常检测，帮助快速定位性能瓶颈。
SME 和 IC 端点均能集成此观测能力，适用于生产环境的调试与优化。
Monitor and debug generative AI inference with SageMaker detailed metrics and Insights dashboard on CloudWatch
Amazon SageMaker AI provides fully managed real-time inference hosting for machine learning models. You deploy a model to a SageMaker endpoint backed by one or more compute instances, and SageMaker handles provisioning and scaling. SageMaker supports multiple endpoint architectures. This post focuses on the two most relevant to generative AI workloads with detailed observability: Single-model endpoints (SME) and Inference component (IC) endpoints.
This report is categorized under AITOP's "How-to" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include SageMaker, CloudWatch, 生成式 AI, 推理.
Primary source verification: published directly by AWS Machine Learning Blog with first-party authority.