InfoQ 中文发布于 08/25 20:12
Cloudflare 将 CI 管道转变为 TypeScript 工作流
Cloudflare 发布了一个 CI SDK，允许开发者使用 TypeScript 而不是 YAML 定义持续集成（CI）管道，并将每个步骤作为持久化的 Cloudflare Workflow 来运行。
InfoQ 中文发布于 08/23 01:17
Cloudflare 推出 Cache Response Rules，在源站响应后进一步控制缓存
Cloudflare 近日推出缓存响应规则（Cache Response Rules），这是一套新的规则引擎，运行在源站返回响应之后、内容写入 Cloudflare 缓存之前。此前，缓存规则（Cache Rules）只能根据请求属性进行判断；缓存响应规则则新增了一个响应处理阶段，可以在响应进入缓存之前检查源站返回的内容。
Microsoft AI Blog发布于 07/21 02:20
Microsoft expands Azure AI and HPC infrastructure with AMD
（翻译）微软借助AMD扩展Azure AI和HPC基础设施
AI workloads are scaling faster than any single infrastructure approach can support — with more models, new agent-driven workloads and surging compute demand driving the need for greater specialization across the stack. To meet this need, Microsoft continues to evolve Azure’s infrastructure, includi
AWS Machine Learning Blog发布于 08/25 03:32
Introducing new Ray capabilities on SageMaker HyperPod
（翻译）在 SageMaker HyperPod 上推出新的 Ray 功能
Amazon SageMaker HyperPod now offers managed Ray support on Amazon EKS. Create and monitor Ray clusters, connect JupyterLab and Code Editor notebooks to live clusters, get out-of-the-box observability, and run resilient distributed training and accelerated inference from SageMaker Studio, all with o
AWS Machine Learning Blog发布于 08/25 00:22
Agentic Resource Discovery (ARD): An open specification for agent discovery
（翻译）Agentic Resource Discovery (ARD)：用于代理发现的开放规范
AWS Agent Registry gives your organization a centralized, searchable catalog for agents, tools, and skills. It works with the open Agentic Resource Discovery (ARD) standard to enable cross-environment discovery and governance at scale.
AWS Machine Learning Blog发布于 08/22 01:06
Agentic Data Operations Platform (ADOP): Data engineering into hours
（翻译）智能体数据运维平台（ADOP）：数小时完成数据工程
The Agentic Data Operations Platform (ADOP) is a reference architecture on Amazon Bedrock that uses specialized AI agents to automate the full Bronze-to-Silver-to-Gold data pipeline lifecycle, compressing new-source onboarding from weeks to hours while keeping data governance and compliance controls
AWS Machine Learning Blog发布于 08/22 01:02
Govern AI agent tool access with Amazon Bedrock AgentCore Gateway
（翻译）使用 Amazon Bedrock AgentCore Gateway 治理 AI 代理工具访问
Give your AI agents governed, auditable access to enterprise tools without consolidating infrastructure. This post walks through a four-scope maturity model (Connect, Control, Catalog, and Harden) for building a governed tool gateway with Amazon Bedrock AgentCore, advancing only when real governance
AWS Machine Learning Blog发布于 08/22 00:59
Reduce RAG costs on Amazon Bedrock with query-aware compression
（翻译）通过查询感知压缩降低 Amazon Bedrock 上的 RAG 成本
Input tokens are often a meaningful part of the cost of running Retrieval Augmented Generation (RAG) at scale. This post describes a query-aware context compression pattern on Amazon Bedrock: after retrieval, a smaller model filters retrieved chunks against the query before the primary model answers
AWS Machine Learning Blog发布于 08/21 05:46
Introducing cross-Region inference for OpenAI GPT-5.6 models on Amazon Bedrock
（翻译）在 Amazon Bedrock 上推出 OpenAI GPT-5.6 模型的跨区域推理
Amazon Bedrock now offers OpenAI GPT-5.6 models (Sol, Terra, and Luna) in more than 25 AWS Regions with cross-Region inference. Learn how US geographic and global inference profiles route requests for higher throughput, how to call the models with the OpenAI and Converse APIs, and how to configure I
AWS Machine Learning Blog发布于 08/21 05:23
Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 3: Visualizing insights with Amazon Quick Sight
（翻译）使用Snowflake、Amazon SageMaker Canvas和Amazon Quick构建无代码ML工作流——第3部分：使用Amazon Quick Sight可视化洞察
In Part 3 of this no-code ML series, you bring fraud detection predictions to life. Import your Amazon SageMaker Canvas predictions into Amazon Quick Sight, build interactive dashboards, use generative BI to answer questions in natural language, and publish AI-generated executive summaries for stake
AWS Machine Learning Blog发布于 08/21 00:24
Scaling agentic AI: Enterprise patterns without vendor lock-in
（翻译）扩展智能体AI：避免供应商锁定的企业模式
Scaling agentic AI across an enterprise requires patterns that preserve flexibility while avoiding vendor lock-in. In this second post of our multi-agent series, we examine how ML teams operate many agentic AI systems across a multi-everything environment of frameworks, models, and providers, and th
AWS Machine Learning Blog发布于 08/26 00:35
Governed reports with Amazon Quick Desktop and Amazon FSx for NetApp ONTAP
（翻译）使用Amazon Quick Desktop和Amazon FSx for NetApp ONTAP实现受治理的报告
Build a governed weekly reporting workflow with Amazon Quick Desktop and Amazon FSx for NetApp ONTAP. An Amazon S3 access point exposes an approved folder to a Quick knowledge base, and a custom skill drafts cited weekly reports and Slack summaries with human review before anything is shared.
AWS Machine Learning Blog发布于 08/26 03:00
Agentic observability with Amazon OpenSearch Service MCP Apps
（翻译）使用 Amazon OpenSearch Service MCP Apps 实现智能体可观测性
Amazon OpenSearch Service now supports MCP Apps, which return interactive visualizations alongside your AI agent's text responses. Learn how a single, locally run MCP server lets your agent move from alert to trace to logs to root cause in one conversation, and how you can verify every step inline w