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10月6日周二
  1. AWS Machine Learning Blog

    Responsible AI governance: How AWS positions customers to align with ISO/IEC 42005:2025

    中文摘要

    AWS投资于有助于客户符合负责任人工智能治理国际标准的工具。在本文中，我们将探讨人工智能系统影响评估：它是什么，如何改善企业范围内的风险管理，以及ISO/IEC 42005:2025如何将进行和记录这些评估的最佳实践标准化。

    英文原文

    AWS invests in tools that help customers align with international standards for responsible AI governance. In this post, we explore the AI system impact assessment: what it is, how it improves enterprise-wide risk management, and how ISO/IEC 42005:2025 codifies best practices for conducting and documenting these assessments.

  2. AWS Machine Learning Blog

    Supercharge regulated workloads with Claude Code and Amazon Bedrock

    中文摘要

    Anthropic Claude Opus 5.5 和 Claude Sonnet 5.5 在 AWS GovCloud（美国）区域的 Amazon Bedrock 上可用。了解如何使用 Claude Code，Anthropic 的代理编码工具，以实现合规对齐的、人工智能辅助的开发，用于受监管和 ITAR 工作负载。

    英文原文

    Anthropic Claude Opus 5.5 and Claude Sonnet 5.5 are available on Amazon Bedrock in the AWS GovCloud (US) Regions. Learn how to use them with Claude Code, Anthropic's agentic coding tool, for compliance-aligned, AI-assisted development on regulated and ITAR workloads.

10月5日周一
  1. AWS Machine Learning Blog

    Downgrading user roles in Amazon Quick

    中文摘要

    Amazon Quick 并没有直接的控制台路径将用户从管理员或作者降级为读者。本文将介绍两种可靠的方法：一种是手动删除并重新创建的方法，另一种是使用 AWS CLI 的降级步骤，该步骤可以安全地降级角色，同时保留资产的所有权。

    英文原文

    Amazon Quick doesn't offer a direct console path to downgrade a user from Admin or Author to Reader. This post walks through two reliable methods: a manual delete-and-recreate approach and an AWS CLI step-down sequence that downgrades roles safely while preserving asset ownership.

10月2日周五
  1. AWS Machine Learning Blog

    Building ambient agents with Amazon Bedrock AgentCore: From event-driven signals to human-in-the-loop workflows

    中文摘要

    环境代理会响应事件，例如 Amazon S3 上传、计划任务或警报，而不是等待聊天提示。本文将介绍如何使用 Amazon Bedrock AgentCore 在 Amazon SQS、AWS Lambda 和 Amazon DynamoDB 上构建与框架无关的环境代理，仅使用一个 ask_human 工具和一个用于人工审核的作业页面。

    英文原文

    Ambient agents respond to events such as an Amazon S3 upload, a schedule, or an alert instead of waiting for a chat prompt. This post walks through building framework-agnostic ambient agents on Amazon Bedrock AgentCore using Amazon SQS, AWS Lambda, and Amazon DynamoDB, with a single ask_human tool and a Jobs page for human-in-the-loop review.

  2. AWS Machine Learning Blog

    Simplify dashboard drill-down with the Amazon Quick Sight hierarchy filter

    中文摘要

    Amazon Quick Sight 是一种完全托管的、原生云业务智能（BI）功能，用于构建和发布交互式仪表板。新的层次过滤器使仪表板作者能够在单一紧凑的控件中实现丰富、多级的过滤功能，减少杂乱，并在更少的步骤中引导读者找到所需的数据。

    英文原文

    Amazon Quick Sight is a fully managed, cloud-native business intelligence (BI) capability for building and publishing interactive dashboards. The new hierarchy filter gives dashboard authors rich, multi-level filtering in a single compact control, reducing clutter and guiding readers to the data they need in fewer steps.

9月30日周三
  1. AWS Machine Learning Blog

    Amazon Bedrock expands Claude model availability to in-country inferencing in India

    中文摘要

    Anthropic 的 Claude Opus 5、Claude Sonnet 5 和 Claude Haiku 4.5 现已通过 Amazon Bedrock 地理跨区域推理在印度推出。您可以在处理印度区域内的数据时访问这些模型，并可通过 Amazon Bedrock 控制台或使用 Messages、InvokeModel 和 Converse API 开始使用。

    英文原文

    Anthropic's Claude Opus 5, Claude Sonnet 5, and Claude Haiku 4.5 are now available in India through Amazon Bedrock geographic cross-Region inference. You can access these models while processing data within the India Regions, and get started from the Amazon Bedrock console or with the Messages, InvokeModel, and Converse APIs.

  2. AWS Machine Learning Blog

    Introducing Anthropic models on Amazon Bedrock for in-region inference in Seoul and Singapore

    中文摘要

    Amazon Bedrock 现在支持 Anthropic 的 Claude Opus 5 和 Claude Sonnet 5，并在首尔提供本地推理支持，同时在新加坡支持 Claude Sonnet 5。如果您在韩国或新加坡有本地数据处理需求，现在可以大规模使用这些 Anthropic 模型，推理过程将完全在您调用的区域内进行。

    英文原文

    Amazon Bedrock now supports Anthropic's Claude Opus 5 and Claude Sonnet 5 with in-region inference in Seoul, and Claude Sonnet 5 in Singapore. If you have local data processing requirements in South Korea or Singapore, you can now use these Anthropic models at scale, with inference processed entirely within the Region you call.