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健康狀態 健康來源類型 官方原文權限 官方原文最近入庫 2026-09-28ID aws-ml-blog運行狀態 已啟用

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最新公開文章

待翻譯:Grok 4.7 is now available on Amazon Bedrock

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:xAI's Grok 4.7 is now available on Amazon Bedrock: a frontier model for coding, long-running agents, and knowledge work. It offers a 500K token context window and four configurable reasoning effort levels, reachable through the Responses, Chat Completions, and Converse APIs.

AWS Machine Learning Blog站內正文待翻譯:Grok 4.7 is now available on Amazon Bedrock

待翻譯:Introducing Claude Sonnet 5.5 on AWS

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Claude Sonnet 5.5 is now available on Amazon Bedrock and Claude Platform on AWS. It's a smarter, more efficient Sonnet model for focused coding and knowledge work, with a lower cost per task at faster speed. This post covers what's new, when to choose Sonnet, and how to get started.

AWS Machine Learning Blog站內正文待翻譯:Introducing Claude Sonnet 5.5 on AWS

待翻譯:Build real-time voice applications with vLLM-Omni on SageMaker AI – Part 1

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Deploy a text-to-speech model on Amazon SageMaker AI with the AWS vLLM-Omni Deep Learning Container and stream generated speech over a persistent bidirectional connection. This Part 1 tutorial deploys Qwen3-TTS and streams speech through a Gradio application.

AWS Machine Learning Blog站內正文待翻譯:Build real-time voice applications with vLLM-Omni on SageMaker AI – Part 1

待翻譯:Generate images and video with vLLM-Omni on SageMaker AI – Part 2

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Deploy two generative media models from one AWS vLLM-Omni Deep Learning Container on Amazon SageMaker AI. Generate an image with FLUX.2-klein through real-time inference, then animate it into video with Wan2.1-VACE through asynchronous inference, and retrieve the MP4 from Amazon S3.

AWS Machine Learning Blog站內正文待翻譯:Generate images and video with vLLM-Omni on SageMaker AI – Part 2

待翻譯:Implementing synthetic monitoring using Amazon Nova Act

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn an agent-driven approach to synthetic monitoring using Amazon Nova Act and Amazon Bedrock AgentCore. The post covers the architecture and patterns for resilient, managed user-journey validation that moves beyond brittle UI scripts, with a complete sample implementation.

AWS Machine Learning Blog站內正文待翻譯:Implementing synthetic monitoring using Amazon Nova Act

待翻譯:Automating Amazon Textract adapter lifecycle management across accounts

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how to operationalize Amazon Textract Custom Queries adapters for production: infrastructure as code with AWS CloudFormation and Terraform, a cross-account adapter promotion process, a pre-classification routing pattern for multiple form versions, and production security controls such as VPC endpoints, encryption, and least-privilege IAM.

AWS Machine Learning Blog站內正文待翻譯:Automating Amazon Textract adapter lifecycle management across accounts

待翻譯:Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP with 40% more throughput

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how to scale Mixture-of-Experts (MoE) reinforcement learning on Amazon EKS using Elastic Fabric Adapter (EFA) and DeepEP. This post presents an architecture that combines Amazon EKS, EFA, and Amazon S3 and increased aggregate reinforcement learning rollout throughput by 40% for large-scale RLHF and GRPO training.

AWS Machine Learning Blog站內正文待翻譯:Scaling MoE reinforcement learning on Amazon EKS with EFA and DeepEP with 40% more throughput

待翻譯:Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how to run SkyRL, an open-source reinforcement learning framework, on Amazon SageMaker HyperPod to post-train a Qwen3-VL-8B vision-language model with GRPO. This walkthrough covers building the container image, launching a Ray cluster from SageMaker Studio, submitting and monitoring the job, and hosting the trained LoRA adapter for inference.

AWS Machine Learning Blog站內正文待翻譯:Accelerate multimodal RL training with SkyRL on Amazon SageMaker HyperPod

待翻譯:NarrateAI: production-ready LLM quality assurance on Amazon Bedrock

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:NarrateAI delivers production-ready LLM quality assurance on Amazon Bedrock. This post details five techniques—adaptive pipeline orchestration, cross-account multi-model failover, real-time streaming evaluation, composite evaluation, and data accuracy verification—that reach about 99% numerical accuracy while streaming responses in real time.

AWS Machine Learning Blog站內正文待翻譯:NarrateAI: production-ready LLM quality assurance on Amazon Bedrock

待翻譯:Deploying real-time personalized speech with Qwen3-TTS on Amazon SageMaker AI

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Deploy the publicly available Qwen3-TTS-12Hz-1.7B-Base text-to-speech model from Amazon SageMaker JumpStart to a fully managed, real-time endpoint, and clone a voice from a short reference clip. Cross-lingual cloning preserves the speaker's identity across languages.

AWS Machine Learning Blog站內正文待翻譯:Deploying real-time personalized speech with Qwen3-TTS on Amazon SageMaker AI

待翻譯:How Datacor built self-service rental analytics with Amazon Quick Sight

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how Datacor built a self-service rental analytics experience for gas and welding distributors by embedding Amazon Quick Sight dashboards and natural language querying into its TrackAbout platform, powered by an automated cross-cloud data pipeline and multi-tenant row-level security.

AWS Machine Learning Blog站內正文待翻譯:How Datacor built self-service rental analytics with Amazon Quick Sight

待翻譯:Multi-Region training with Amazon SageMaker HyperPod and Qumulo

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Amazon SageMaker HyperPod and Cloud Native Qumulo let you place training compute in one AWS Region while keeping your dataset in another. This post shares the architecture and validation results from a cross-Region training run, where a remote cluster matched a co-located cluster's throughput after a brief NeuralCache warmup.

AWS Machine Learning Blog站內正文待翻譯:Multi-Region training with Amazon SageMaker HyperPod and Qumulo

待翻譯:Speaker-labeled transcription with WhisperX on SageMaker AI

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:The AWS WhisperX Deep Learning Container packages Whisper, wav2vec2 forced alignment, and speaker diarization into a GPU-ready image. Learn how to deploy it to Amazon SageMaker AI real-time and asynchronous endpoints for word-level, speaker-labeled transcription, plus the production details that matter: the GPU AMI pin, scaling, and cost controls.

AWS Machine Learning Blog站內正文待翻譯:Speaker-labeled transcription with WhisperX on SageMaker AI

待翻譯:Build a multi-account AI agent with AgentCore Gateway and MCP

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Build a multi-account architecture that keeps each team's data in its own AWS account while giving AI agents a unified way to query across them. A central platform account runs the agent using Amazon Bedrock AgentCore Gateway and MCP, while line-of-business accounts expose their data as MCP servers with secure cross-account access and fine-grained authorization.

AWS Machine Learning Blog站內正文待翻譯:Build a multi-account AI agent with AgentCore Gateway and MCP

待翻譯:Aderant builds intelligent ticket triage with Amazon Nova

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how Aderant built an intelligent ticket triage system on Amazon Nova Lite through Amazon Bedrock, automating context gathering, classification, routing, and knowledge enrichment for its cloud operations team.

AWS Machine Learning Blog站內正文待翻譯:Aderant builds intelligent ticket triage with Amazon Nova

待翻譯:From portal-hopping to instant answers: HEMA’s journey with MCP and Amazon Bedrock

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:HEMA, a 100-year-old Dutch retailer, turned developer portal-hopping into instant answers by building HAL, an internal AI assistant on Amazon Bedrock AgentCore. Using Model Context Protocol (MCP), HAL delivers governed knowledge inside the tools teams already use, with no AWS credentials on the client and security anchored in Microsoft Entra ID.

AWS Machine Learning Blog站內正文待翻譯:From portal-hopping to instant answers: HEMA’s journey with MCP and Amazon Bedrock

待翻譯:Agentic conversational video intelligence built on AWS

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how to build a conversational video intelligence solution on AWS using an agentic architecture. A single Strands Agents SDK agent orchestrates Amazon Bedrock, Amazon Rekognition, and Amazon Transcribe at runtime, deciding which service to call so you can ask natural language questions about your videos and get answers in seconds.

AWS Machine Learning Blog站內正文待翻譯:Agentic conversational video intelligence built on AWS

待翻譯:Use open weight models as your AI coding agent with Amazon Bedrock

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Pair OpenCode, an open-source terminal-native AI coding agent, with open weight models on Amazon Bedrock to get a secure, flexible, pay-per-use coding assistant. Learn how to configure multi-model workflows, match the right model to each task, and keep your data in your own AWS account with no infrastructure to manage.

AWS Machine Learning Blog站內正文待翻譯:Use open weight models as your AI coding agent with Amazon Bedrock

待翻譯:Claude Opus 5.5 is now available on AWS

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Claude Opus 5.5, Anthropic's most capable Opus model for agentic coding, knowledge work, and long-running tasks, is now available on Amazon Bedrock and Claude Platform on AWS. This post covers what's new in Opus 5.5, practical guidance, and how to start building with the model on Amazon Bedrock.

AWS Machine Learning Blog站內正文待翻譯:Claude Opus 5.5 is now available on AWS

待翻譯:Evaluate skill-equipped agents with Strands Evals and Amazon Bedrock AgentCore

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Skills let you encode domain-specific procedures as reusable, portable instructions for agents, but a fluent answer doesn't prove the agent picked the right skill or followed it. Learn how to measure skill selection and instruction following with Strands Evals and Amazon Bedrock AgentCore Evaluations.

AWS Machine Learning Blog站內正文待翻譯:Evaluate skill-equipped agents with Strands Evals and Amazon Bedrock AgentCore

待翻譯:How Reactiv automates mobile commerce 80% faster with Amazon Bedrock AgentCore

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Reactiv used Amazon Bedrock AgentCore to build a multi-agent AI Scheduler that autonomously refreshes Shopify merchants' mobile apps on a schedule, reducing merchant configuration time by 80% and getting to production 33% faster.

AWS Machine Learning Blog站內正文待翻譯:How Reactiv automates mobile commerce 80% faster with Amazon Bedrock AgentCore

待翻譯:Right-size generative AI endpoints with concurrency sweeps on Amazon SageMaker AI

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Concurrency sweeps help you right-size a generative AI endpoint on Amazon SageMaker AI by systematically benchmarking it at increasing load levels. This post walks through deploying a model, running automated concurrency sweeps with the CreateAIBenchmarkJob API, and using the results to make data-driven capacity decisions about fleet size.

AWS Machine Learning Blog站內正文待翻譯:Right-size generative AI endpoints with concurrency sweeps on Amazon SageMaker AI

待翻譯:How Trane gets building insights 60x faster with Amazon Bedrock AgentCore

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:In about four weeks, Trane Technologies built an AI-powered agentic solution on Amazon Bedrock AgentCore that reduced a 20-minute, multi-screen building diagnostic workflow to a 20-second natural language interaction, a 60x improvement in time-to-insight. This post shares the architectural approach and key design decisions behind the solution.

AWS Machine Learning Blog站內正文待翻譯:How Trane gets building insights 60x faster with Amazon Bedrock AgentCore

待翻譯:How Tata Elxsi detects industrial safety risks in seconds on AWS

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how Tata Elxsi built IRIS, a real-time industrial safety platform on AWS. IRIS filters camera video at the edge, streams metadata through Amazon Kinesis, runs computer vision on Amazon SageMaker AI, and correlates detections into high-confidence alerts, detecting unsafe conditions in seconds instead of minutes.

AWS Machine Learning Blog站內正文待翻譯:How Tata Elxsi detects industrial safety risks in seconds on AWS

待翻譯:Extending public sector intelligence with Agentforce and AWS

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Public sector agencies process large volumes of unstructured evidence, such as body camera footage and scanned documents. This post shows how to combine Amazon Bedrock Data Automation with the Model Context Protocol (MCP) to turn that data into structured insights and surface them through natural language queries in Salesforce Agentforce.

AWS Machine Learning Blog站內正文待翻譯:Extending public sector intelligence with Agentforce and AWS

待翻譯:xAI’s Grok 4.6 is now available in Amazon Bedrock

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:xAI's Grok 4.6 is now available in Amazon Bedrock: a frontier model for long-running agents, coding, and knowledge work, with a 500K token context window and four reasoning effort levels. It runs on both the bedrock-mantle and bedrock-runtime endpoints, with Converse API and cross-Region inference support.

AWS Machine Learning Blog站內正文待翻譯:xAI’s Grok 4.6 is now available in Amazon Bedrock

待翻譯:How BMW Group detects cost anomalies across 14,000 cloud accounts

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:BMW Group operates CLEA, a FinOps platform monitoring more than 14,000 cloud accounts. This post shows how BMW added automated daily cost anomaly detection, moving from reactive dashboards to proactive alerts using Prophet forecasting, AWS Step Functions, and a serverless pipeline that processes every account for about $50 per month.

AWS Machine Learning Blog站內正文待翻譯:How BMW Group detects cost anomalies across 14,000 cloud accounts

待翻譯:Run Positron on Amazon SageMaker AI for data science workflows

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Positron, Posit's IDE for data science, now runs on Amazon SageMaker AI. This post shows how a data scientist explores an Amazon Athena table, validates features in R, trains an XGBoost model in Python, deploys a real-time SageMaker AI endpoint, and reports results with Quarto, all in one governed SageMaker Studio Space.

AWS Machine Learning Blog站內正文待翻譯:Run Positron on Amazon SageMaker AI for data science workflows

待翻譯:How Benchling secured multi-tenant AI agents with Amazon Bedrock AgentCore

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how Benchling built a defense-in-depth security architecture to run untrusted, AI agent-generated scientific code across thousands of life sciences tenants using Amazon Bedrock AgentCore Code Interpreter in VPC mode, combined with Amazon Route 53 Resolver DNS Firewall and VPC endpoint policies to block data exfiltration, including through DNS.

AWS Machine Learning Blog站內正文待翻譯:How Benchling secured multi-tenant AI agents with Amazon Bedrock AgentCore

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