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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

    Best practices for Amazon SageMaker HyperPod administration and governance

    中文摘要

    通过 Amazon SageMaker Unified Studio 管理 Amazon SageMaker HyperPod 的方法,同时保持集群治理。本文展示了平台团队如何设计基础设施边界、治理访问权限、分配共享容量,并在组织、项目、集群和工作负载控制层面上一致地运行 HyperPod。

    英文原文

    Learn how to administer Amazon SageMaker HyperPod through Amazon SageMaker Unified Studio while preserving cluster governance. This post shows platform teams how to design infrastructure boundaries, govern access, allocate shared capacity, and operate HyperPod consistently across the organization, project, cluster, and workload control layers.

  3. AWS Machine Learning Blog

    Manage Amazon SageMaker HyperPod Spaces directly from SageMaker Studio

    中文摘要

    数据科学家和机器学习工程师现在可以直接从SageMaker Studio在SageMaker HyperPod EKS集群上创建、配置、启动、停止和打开Amazon SageMaker Spaces。只需点击几下即可启动JupyterLab和代码编辑器环境,而无需使用命令行工具。

    英文原文

    Data scientists and ML engineers can now create, configure, start, stop, and open Amazon SageMaker Spaces on SageMaker HyperPod EKS clusters directly from SageMaker Studio. Launch JupyterLab and Code Editor environments in a few clicks, without using command-line tools.

  4. AWS Machine Learning Blog

    Build a voice travel concierge with Amazon Bedrock AgentCore, Managed Knowledge Base and Nova Sonic

    中文摘要

    通过 Amazon Bedrock AgentCore、Amazon Nova Sonic(用于实时语音)以及 Amazon Bedrock Knowledge Bases(用于政策解答),将语音旅行礼宾服务添加到航空公司应用程序中。旅客可以通过语音更改座位、查看延误信息并询问政策问题,而该代理通过 MCP 工具连接到您的后端,并在进行任何更改之前确认更改内容。

    英文原文

    Add a voice travel concierge to an airline app with Amazon Bedrock AgentCore, Amazon Nova Sonic for real-time speech, and Amazon Bedrock Knowledge Bases for policy answers. Travelers change seats, check delays, and ask policy questions by voice, while the agent reaches your backend through MCP tools and confirms every change before it writes.

  5. AWS Machine Learning Blog

    Introducing GLM 5.3 on Amazon Bedrock

    中文摘要

    Z.ai 的 GLM 5.3 现已在 Amazon Bedrock 上可用:这是一个用于编码和长周期代理任务的 753B 参数专家混合模型。了解如何通过 OpenAI 兼容的 API 调用它,通过提示缓存来降低成本和延迟,并使用开源的 Strix 代理进行授权安全测试。

    英文原文

    GLM 5.3 from Z.ai is now available on Amazon Bedrock: a 753B-parameter mixture-of-experts model built for coding and long-horizon agentic tasks. Learn how to invoke it with the OpenAI-compatible APIs, cut cost and latency with prompt caching, and run an authorized security test with the open-source Strix agent.

  6. 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.

  7. AWS Machine Learning Blog

    New agent skill: Amazon SageMaker optimized generative AI inference for your coding agent

    中文摘要

    Amazon SageMaker优化的生成式AI推理通过AWS的Agent Toolkit引入了aws-ai-ml技能,使像Kiro、Claude Code和Codex这样的编码代理具备深入的推理优化和基准测试专业知识。描述你想要的内容,你的代理将生成可执行的SageMaker Python SDK v3代码,用于基准测试、推荐和比较部署。

    英文原文

    Amazon SageMaker optimized generative AI inference introduces the aws-ai-ml skill through the Agent Toolkit for AWS, giving coding agents like Kiro, Claude Code, and Codex deep expertise in inference optimization and benchmarking. Describe what you want, and your agent generates executable SageMaker Python SDK v3 code to benchmark, recommend, and compare deployments.

10月5日周一
  1. AWS Machine Learning Blog

    Making Amazon Quick enterprise-ready: Automated, auditable cross-account resource promotion

    中文摘要

    将 Amazon Quick 的资源(代理、操作连接器、知识库、流程和空间)从开发环境推广到生产环境 AWS 账户一直是一个手动且容易出错的过程。本文展示了如何通过在 Amazon Bedrock AgentCore 上使用可重复、可审计的 MCP 服务器来实现跨账户的自动化推广。

    英文原文

    Promoting Amazon Quick resources (agents, action connectors, knowledge bases, flows, and spaces) from a development to a production AWS account has been a manual, error-prone chore. This post shows how to automate cross-account promotion with an idempotent, auditable MCP server on Amazon Bedrock AgentCore.

  2. AWS Machine Learning Blog

    Agentic retrieval with LangChain and Amazon Bedrock Knowledge Bases

    中文摘要

    在 Amazon Bedrock 受管理的知识库上构建一个检索增强生成(RAG)应用程序,使用 LangChain,并查看代理检索如何处理单次检索回答不好的多部分问题。通过两种路径运行相同的查询,阅读跟踪事件,并比较每种检索路径的成本。

    英文原文

    Build a Retrieval Augmented Generation (RAG) application on Amazon Bedrock Managed Knowledge Base with LangChain, and see how agentic retrieval handles the multi-part questions that single-shot retrieval answers poorly. Run the same query through both paths, read the trace events, and compare what each retrieval path costs.

  3. 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.

  4. AWS Machine Learning Blog

    Evaluating multi-agent systems for explainability and helpfulness with Amazon Bedrock AgentCore

    中文摘要

    多智能体系统需要比流畅响应更深入的保障:它们必须选择正确的工具,遵守约束条件,并解释其决策。了解如何构建基于Strands的多智能体供应链决策系统,并使用Amazon Bedrock AgentCore评估,通过内置、自定义和可解释性评估器对其进行评估。

    英文原文

    Multi-agent systems need deeper guarantees than fluent responses: they must select the right tools, respect constraints, and explain their decisions. Learn how to build a Strands-based multi-agent supply chain decisioning system and evaluate it with Amazon Bedrock AgentCore Evaluations using built-in, custom, and explainability evaluators.

10月2日周五
  1. AWS Machine Learning Blog

    Sweep thousands of leases for compliance using Amazon Quick and the Adjudicated Query pattern

    中文摘要

    已裁定查询模式将Amazon Quick聊天代理与受约束的MCP服务器通过确定性规则引擎配对,以提供可证明完整且有据可依的合规性答案。本文将逐步介绍参考架构和可部署的AWS CDK示例,以租赁合规性为例进行说明。

    英文原文

    The Adjudicated Query pattern pairs the Amazon Quick chat agent with a bounded MCP server over a deterministic rules engine to deliver provably complete, defensible compliance answers. This post walks through the reference architecture and a deployable AWS CDK sample, using lease compliance as the running example.

  2. AWS Machine Learning Blog

    Add secure Web Search to Claude Desktop with Amazon Bedrock AgentCore

    中文摘要

    Amazon Bedrock 上的 Claude Desktop 仅限于该模型的知识截止日期,不支持网络搜索。在本文中,我们将逐步介绍如何使用 Amazon Bedrock AgentCore 网关将 Claude Desktop 连接到网络搜索,通过 AWS IAM Identity Center 和 Amazon Cognito 进行基于 JWT 的入站身份验证。

    英文原文

    Claude Desktop on Amazon Bedrock is limited to the model's knowledge cutoff without web search. In this post, we walk through connecting Claude Desktop to Web Search using Amazon Bedrock AgentCore Gateway, with JWT-based inbound authentication through AWS IAM Identity Center and Amazon Cognito.

  3. AWS Machine Learning Blog

    Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI

    中文摘要

    微调可以教会一个小型搜索代理你的工具和环境,使其在更低的延迟和成本下,具备前沿模型的可靠性。在本文中,我们使用多轮强化学习(MTRL)在Amazon SageMaker AI上对一个由大型语言模型驱动的搜索代理进行微调,并分享我们在检索质量和可靠性方面测量到的提升。

    英文原文

    Fine-tuning teaches a small search agent your tools and environment, giving it the reliability of a frontier model at lower latency and cost. In this post, we fine-tune an LLM-powered search agent with multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI and share the gains we measured in retrieval quality and reliability.

  4. AWS Machine Learning Blog

    Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore

    中文摘要

    了解AWS专业服务如何利用基于Amazon Bedrock AgentCore的多智能体框架,实现企业级云迁移的端到端自动化。专门构建的AI智能体负责发现、基础设施即代码生成、组合治理以及迁移后操作,将IaC开发时间从数周缩短至数分钟。

    英文原文

    Learn how AWS Professional Services uses a multi-agent framework built on Amazon Bedrock AgentCore to automate enterprise cloud migrations end to end. Purpose-built AI agents handle discovery, infrastructure as code generation, portfolio governance, and post-migration operations, reducing IaC development time from weeks to minutes.

  5. AWS Machine Learning Blog

    Serve live, governed data in AI-built apps with Amazon Quick

    中文摘要

    在 Amazon Quick 中,使用实时数据的应用程序,AI 构建的应用程序会实时查询您受控的 Quick Sight 数据集,而不是静态的、在构建时拍摄的快照。每次查询都会以查看应用程序的人员身份运行,因此行级和列级的安全性会根据每个读者进行应用。了解如何使用自然语言构建、发布和共享实时数据应用程序。

    英文原文

    With Live Data in Apps in Amazon Quick, AI-built apps query your governed Quick Sight datasets in real time instead of static, build-time snapshots. Each query runs as the person viewing the app, so row-level and column-level security apply per reader. Learn how to build, publish, and share a live-data app using natural language.

  6. AWS Machine Learning Blog

    Build agent memory with NVIDIA NeMo Agent Toolkit and Amazon S3 Vectors

    中文摘要

    了解如何在NVIDIA NeMo Agent Toolkit(NAT)中使用Amazon S3向量作为持久化内存层,该工具在Amazon Elastic Kubernetes Service(Amazon EKS)上部署。本文将展示NAT的内存子系统的工作原理,并通过一个多元代理投资研究的用例,说明如何使用Amazon S3向量作为自定义内存提供程序。

    英文原文

    Learn how to use Amazon S3 Vectors as the persistent memory layer within the NVIDIA NeMo Agent Toolkit (NAT), deployed on Amazon Elastic Kubernetes Service (Amazon EKS). This post shows how NAT's memory subsystem works and how to implement Amazon S3 Vectors as a custom memory provider, using a multi-agent investment research use case.

  7. AWS Machine Learning Blog

    Uplifting conversion across the acquisition funnel with personalization using contextual bandits on AWS

    中文摘要

    生成式AI使得大规模生产个性化内容变得成本低廉,但你该向每个客户展示哪种变体呢?亚马逊支付(Amazon Payments)在Amazon SageMaker AI上使用了多目标上下文老虎机算法来个性化获取流程,使某一受众的单位数转化率有所提升,并且了解到是内容而非模型成为了限制因素。

    英文原文

    Generative AI makes it cheap to produce personalized content at scale, but which variation do you show each customer? Amazon Payments used a multi-objective contextual bandit on Amazon SageMaker AI to personalize an acquisition funnel, achieving a high single-digit conversion lift for one audience, and learning why content, not the model, was the constraint.

  8. 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.

  9. AWS Machine Learning Blog

    Implementing Multi-Environment Access for Claude Platform on AWS

    中文摘要

    了解如何通过单一订阅在AWS上配置对Claude平台的安全、多环境访问:跨账户SigV4用于AWS工作负载,开发人员的工作区作用域API密钥,以及外部环境的OIDC联合,所有操作都在专用AI服务账户中实现工作区级别的隔离。

    英文原文

    Learn how to configure secure, multi-environment access to Claude Platform on AWS from a single subscription: cross-account SigV4 for AWS workloads, workspace-scoped API keys for developers, and OIDC federation for external environments, with workspace-level isolation in a dedicated AI Services account.