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  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  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

    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.

  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

    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.

  6. 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.

  1. AWS Machine Learning Blog

    Query claims in natural language with Amazon Bedrock Knowledge Bases

    中文摘要

    这个技术教程在 Amazon Bedrock 知识库上构建了一个对话式声明助手,该助手能够用引用的方式回答自然语言问题。本文档涵盖了从 Amazon S3 导入声明文档、使用 AgenticRetrieveStream API 进行查询、多轮跟进、元数据过滤以及上下文基础的防护措施。

    英文原文

    This technical how-to builds a conversational claims assistant on Amazon Bedrock Knowledge Bases that answers natural-language questions with citations. It covers ingesting claim documents from Amazon S3, querying with the AgenticRetrieveStream API, multi-turn follow-ups, metadata filters, and contextual grounding guardrails.

  2. AWS Machine Learning Blog

    Build a multi-agent music production pipeline on Amazon Bedrock AgentCore Runtime Instances

    中文摘要

    Amazon Bedrock AgentCore 运行时实例提供多智能体工作流,使用 AWS 管理的带有 GPU 的 EC2 基础设施,具有持久化卷和多天会话。在本文中,我们部署了一个三智能体的音乐制作流程,这些智能体在同一块 GPU 实例上共存,共享文件系统,并相互传递工作以生成最终的音乐作品。

    英文原文

    Amazon Bedrock AgentCore Runtime Instances gives multi-agent workflows AWS managed EC2 infrastructure with GPUs, persistent volumes, and multi-day sessions. In this post, we deploy a three-agent music production pipeline where the agents colocate on one GPU instance, share a filesystem, and hand work to each other to produce a finished track.

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