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10月6日周二
  1. 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.

  2. 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.

  3. 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2日周五
  1. 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.