Solidot发布于 08/24 22:48

微软删除了逾 17 万非营利组织的数据

Solidot是至顶网的科技资讯网站,主要面对开源自由软件和关心科技资讯读者群,包括众多中国开源软件的开发者,爱好者和布道者。口号是“奇客的知识,重要的东西”。

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InfoQ 中文发布于 08/22 23:15

Cloudflare 推出 Agent Tracing:支持截断限制,不同框架的 Payload 默认记录策略存在差异

Cloudflare 推出了 Cloudflare Agents 的首个组件,Agent Tracing(Agent 追踪),可提供用于集中查看已经部署的 Agent 会话的统一 Dashboard。此次发布在现有 Workers Tracing 的基础上增加了 Agent 级别的 Span,同时也伴随着明确的收费时间表:Beta 期间免费,2026 年 10 月 1 日起将纳入 Workers Observability 的计费体系。

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AWS Machine Learning Blog发布于 08/25 02:59

Democratizing institutional knowledge: Building an AI-powered knowledge management system with AWS

(翻译)普及机构知识:使用 AWS 构建 AI 驱动的知识管理系统

Learn how to build a customizable, smart-caching knowledge management system on AWS that captures and delivers institutional (tribal) knowledge through a voice-first AI avatar. The accelerator uses Amazon Bedrock Knowledge Bases for retrieval-augmented generation and deploys in hours with AWS CloudF

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NVIDIA Technical Blog发布于 08/24 23:00

Maximizing AI Factory Performance per Watt with NVIDIA DSX MaxLPS

(翻译)借助 NVIDIA DSX MaxLPS 最大化 AI 工厂每瓦性能

AI factories are power-constrained industrial systems. The question is no longer how many GPUs fit in a data center, but how much AI output each available…

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AWS Machine Learning Blog发布于 08/25 00:13

Building a restaurant telephony AI host with Amazon Connect

(翻译)使用 Amazon Connect 构建餐厅电话 AI 接待员

Learn how to build a voice ordering system for restaurants that answers a phone call and takes an order end to end, with no app, no website, and no sign-in. It uses Amazon Connect for telephony, Amazon Connect Agentic Voice for real-time speech, an Amazon Connect AI agent for reasoning, and Amazon B

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AWS Machine Learning Blog发布于 08/24 23:53

AI-powered metadata correction and harmonization

(翻译)AI 驱动的元数据修正与协调

Metadata harmonization (standardizing labels, identifiers, and formats so datasets can work together) is still largely manual. This post shows how AI-powered metadata correction works in practice, covering two approaches, human-in-the-loop validation and autonomous agent-driven workflows, plus gover

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AWS Machine Learning Blog发布于 08/22 00:57

Accelerating aircraft IFEC diagnostics with agentic AI on AWS

(翻译)借助 AWS 上的智能体 AI 加速飞机 IFEC 诊断

Panasonic Avionics worked with AWS and the AWS Generative AI Innovation Center to build an agentic AI system on Amazon Bedrock, Amazon SageMaker, and AWS Glue that diagnoses in-flight entertainment and connectivity (IFEC) issues across a global fleet, reducing diagnosis time from hours to minutes wh

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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 2: Data preparation and model building with Amazon SageMaker Canvas

(翻译)使用Snowflake、Amazon SageMaker Canvas和Amazon Quick构建无代码机器学习工作流——第2部分:使用Amazon SageMaker Canvas进行数据准备和模型构建

In Part 2 of this no-code ML series, you connect Amazon SageMaker Canvas to Snowflake, prepare and join transaction data with Data Wrangler visual transformations, and train an XGBoost fraud detection model. All without writing machine learning code, laying the groundwork for interactive dashboards

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AWS Machine Learning Blog发布于 08/21 00:06

AWS vector solutions: Build agentic AI where your data lives

(翻译)AWS 向量解决方案:在数据所在之处构建智能体 AI

AWS offers a broad portfolio of vector search built directly into the databases and storage services you already use, with no standalone vector database or data migration required. This post covers six purpose-built services, a decision framework for choosing the right engine, and customer proof poi

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AWS Machine Learning Blog发布于 08/20 06:09

Automate Document Processing with Quick Automate and the IDP Accelerator

(翻译)利用 Quick Automate 与 IDP Accelerator 自动化文档处理

Classifying, extracting, and validating high volumes of documents is a challenge across banking, insurance, healthcare, and the public sector. See how a mid-size mortgage lender automates its entire document intake pipeline, from email to validated data, using the AWS GAIIC IDP Accelerator and Amazo

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InfoQ 中文发布于 08/26 19:11

Netflix详述如何扩展其实时服务地图

Netflix描述了如何重新设计驱动Service Topology(实时服务依赖地图)的流处理管道以满足生产规模需求。该系统采用三阶段架构,将中间解析过程与丰富和持久化过程实现了分离,将回压传播回Kafka而不是丢弃记录,并在高流量内部传输时使用服务器发送事件(server-sent event)来取代gRPC。

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