---
格式版本: 2
标题: "Artificial Intelligence"
原文链接: "https://aws.amazon.com/cn/blogs/machine-learning/"
发布日期: "2026-06-30"
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发布时间仲裁尝试次数: 0
发布时间仲裁耗时毫秒: 0
发现时间: "2026-08-12T13:17:59+08:00"
入库时间: "2026-08-12T05:27:29.129Z"
来源平台: "固定入口"
搜索渠道: "fixed_url"
搜索词: "https://aws.amazon.com/blogs/aws/"
匹配关键词:
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相关厂家:
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  - "OpenAI"
相关专家:
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内容类型: "网页"
抓取工具: "Free Fetch + Defuddle"
清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取"
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AI优质: "否"
AI打分: 10
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "该页面为AWS机器学习博客入口页，仅有AI应用、模型部署等文章列表，未涉及超节点/AI Rack/机柜级AI基础设施、核心部件、供电、散热、高速互连或量产落地等任何项目关注内容，主题完全无关。"
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AI新颖性: 2
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采集批次: "2026年8月12日13点00分43秒"
采集批次ID: "20260812-130043-786"
去重键: "https://aws.amazon.com/cn/blogs/machine-learning"
---

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It’s our goal for AWS to be the most secure place to run any workload, and in support of that we’ve been deeply investing in security across our services since AWS’s inception more than two decades ago. Our AI services like Amazon Bedrock are built on this foundation and with the same focus. 

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Frontier teams are not just using AI to code faster. They’re redesigning how software gets built. The result is 4.5x productivity gains, in some cases more than 10x.

[![](https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/08/11/Screenshot-2026-08-11-at-4.37.01%E2%80%AFPM-1024x571.png)](https://aws.amazon.com/blogs/machine-learning/accelerate-cyber-defense-with-openai-and-aws-daybreak-red-daybreak-blue-now-available-to-eligible-customers-on-amazon-bedrock/)

Daybreak Red and Daybreak Blue from OpenAI, specialized cyber defense models from OpenAI, are now available on Amazon Bedrock to eligible customers. Both models run with zero-operator access enforced at the chip, keeping your code and vulnerability data secure.

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ONESTRUCTION, with technical advisory from the AWS Generative AI Innovation Center, built Ishigaki-IDS, a foundation model specialized for construction and BIM workflows. This architectural case study shows how they combined synthetic data, a three-stage training pipeline, and verifiable rewards on Amazon EC2 to build a domain model in a data-scarce field.

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Photographers are among the most skeptical audiences for generative AI. Learn how Pixieset used Amazon Bedrock to launch an AI-generated alt text feature to millions of users in four months, reaching 35% adoption by automating the tedious image SEO work photographers avoid, without touching the creative craft they take pride in.

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Learn how First Orion, a branded communications company, shifted from brittle script-based UI testing to AI-driven QA automation with Amazon Nova Act. By describing tests in plain English instead of maintaining selector-based code, they cut QA cycle times, freed engineering capacity, and caught regressions earlier.

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Claude apps gateway is a self-hosted governance layer between Claude Code and Claude Desktop and Amazon Bedrock or Claude Platform on AWS. This post presents a production reference deployment covering end-to-end architecture, enterprise deployment patterns, cost, and implementation resources.

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The Amazon SageMaker AI Spaces add-on for Amazon EKS runs managed JupyterLab and Code Editor environments on the cluster your ML team already operates. This post shows how to install and configure the add-on, connect from the browser and from VS Code over SSH-over-SSM, and move your team to OpenID Connect sign-in with Amazon Cognito.

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nOps rebuilt its Clara FinOps AI agent on Amazon Bedrock AgentCore, replacing a self-managed Amazon EKS stack running LangChain and LangGraph. The move cut time-to-production by 75% (from 10-12 months to 4 months), improved response quality, and reduced operational overhead while keeping analytics governed through Databricks Lakehouse Metric Views.

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In this post, you learn how Cohere Health built a multi-tenant agentic architecture on AgentCore using AgentCore Runtime’s secure MicroVM isolation, unified tool access through AgentCore Gateway, AgentCore Memory, and the Agent Skills open standard to rapidly scale policy digitization capabilities, while preserving transparency, version control, and human oversight.

[![](https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2026/08/07/ml-21106.png)](https://aws.amazon.com/blogs/machine-learning/how-trends-automates-root-cause-analysis-with-amazon-bedrock/)

TReNDS, a research center at Georgia State University, built an agentic AI pipeline on Amazon Bedrock and the open-source Strands Agents SDK that automatically investigates production errors in real time, reducing root-cause analysis from 15 to 30 minutes of manual work to under 60 seconds.

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The AWS Generative AI Innovation Center built an automated system that uses constraint programming and custom tree search to determine, with mathematical certainty, when and how an NHL team clinches a playoff spot. The approach was validated against four full NHL seasons of officially published results.