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Lead Story
Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations
Multi-agent systems fail in ways traditional monitoring misses. This post presents a dual-layer approach to monitoring production agents: Amazon Bedrock AgentCore Evaluations for continuous quality scoring and AWS DevOps Agent for autonomous infrastructure investigation, shown on a four-agent airline reservation system.
AWS Machine Learning Blog / 9:58 PM
Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference
Amazon SageMaker Inference now offers prefix-aware routing, a routing strategy that sends requests sharing the same prompt prefix to the same instance so the KV cache stays warm. In benchmarks on Llama 3.1 70B, it reduced P50 time-to-first-token by up to 77% and raised KV cache hit rates from about 25% to over 80%.
AWS Machine Learning Blog / 9:37 PM
Reduce inference cold starts on Amazon SageMaker HyperPod with model caching
Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images onto cluster nodes so pods read from local NVMe storage instead of downloading over the network. Learn how model caching cuts cold starts from tens of minutes to seconds, how it works, and how to enable it.
AWS Machine Learning Blog / 9:15 PM
Video and image search in Amazon Bedrock Knowledge Base using Marengo 3.0
TwelveLabs Marengo Embed 3.0 is now generally available as an embedding model in Amazon Bedrock Knowledge Bases, bringing fully managed natural language search to video, image, and audio content. This walkthrough shows how to build a knowledge base powered by Marengo 3.0 and run semantic queries against your media.
AWS Machine Learning Blog / 6:16 PM
Amazon Quick is now generally available on desktop
Your teams get an AI assistant that handles real work while your data stays in your environment and your conversations stay private Today, the Amazon Quick desktop application is generally available on macOS and Windows. We’re also adding a new activity feed to the mobile experience on iOS and Android that consolidates email, calendar, CRM, […]
AWS Machine Learning Blog / 4:02 PM
Model-agnostic PII detection with LLMs
A configurable, model-agnostic detector that turns any large language model on Amazon Bedrock into a PII detector. Because the entities to detect live in a prompt rather than in code, one detector adapts to new entity types without retraining, and it outperforms an off-the-shelf tool across five public corpora and nine LLM-based detectors.
AWS Machine Learning Blog / 3:53 PM
How AvioBook builds turnaround insights from operational data with Amazon Bedrock AgentCore
AvioBook, a Thales Group Company, prototyped Connected Analytics on Amazon Bedrock AgentCore to turn AvioBook Connect's operational data into plain-language, evidence-based answers for airline managers and dispatchers, helping them find and act on the causes of flight turnaround delays.
AWS Machine Learning Blog / 10:06 PM
Take on your most ambitious work with GPT-6 Astra on Amazon Bedrock
GPT-6 Astra from OpenAI is now generally available on Amazon Bedrock. It brings deeper reasoning and sharper judgment to your most demanding tasks, running on the Amazon Bedrock inference engine built for high performance, security, and scale.
AWS Machine Learning Blog / 4:21 PM
Benchmarking small LLM inference on SageMaker AI: G7 vs G5 and G6
Benchmark two 30B Mixture-of-Experts models, Qwen3-Coder-30B and NVIDIA Nemotron-3-Nano-30B, across G5, G6, G6e, and G7 GPU instances on Amazon SageMaker AI. Compare throughput, latency, and cost-per-token, and see how G7's NVIDIA Blackwell GPUs deliver measurable price-performance gains for real-time LLM inference.
AWS Machine Learning Blog / 4:08 PM
Customizing your knowledge base on Amazon Bedrock for large and complex documents using Amazon Textract
Learn how to customize an Amazon Bedrock knowledge base for large, complex documents by combining the high-accuracy text extraction of Amazon Textract with the generative AI of Amazon Bedrock. This post shows how to ingest and preprocess PDFs and images, then query utility bills at scale for faster, more accurate customer interactions.
AWS Machine Learning Blog / 4:11 PM
Integrating Outlook with Amazon Quick for AI-powered email automation
Integrate Microsoft Outlook with Amazon Quick to automate email management, calendar scheduling, and workflow coordination. This post walks through the end-to-end setup and shows automation scenarios using Amazon Quick chat agents, Amazon Quick Flows, and Amazon Quick Automate.
AWS Machine Learning Blog / 7:12 PM
Introducing Claude Fable 5.1 on AWS
Claude Fable 5.1 is now available on Amazon Bedrock and Claude Platform on AWS. This post covers Claude Fable 5.1's improvements, the Enterprise Frontier Safeguards for keeping your data in a cloud environment you control, and how to start building with the model on Amazon Bedrock.
AWS Machine Learning Blog / 3:45 PM
How Boomi Scribe streamlines documentation using AWS
Boomi Scribe is an AI-powered agent on AWS that automatically generates documentation for enterprise integration workflows. Learn how Boomi uses Amazon Bedrock, Amazon SageMaker AI, Amazon S3, Amazon DynamoDB, and AWS Lambda to parse integration DAGs, generate detailed documentation, and compare component versions at scale.
AWS Machine Learning Blog / 11:04 PM
Build agentic creative workflows with Amazon Quick and fal
Creative teams produce more assets than ever, but fragmented tools and manual context transfer slow production. This post shows how to build a reusable agent harness with Amazon Quick and fal, connected through the Model Context Protocol (MCP), using two hands-on workflows: an eight-panel storyboard and a music-video concept prototype.
AWS Machine Learning Blog / 4:05 PM
Reduce ASR inference costs by 75% with NVIDIA MPS on Amazon EC2
Serving automatic speech recognition (ASR) models at scale is costly when each request uses only a fraction of a GPU. Learn how NVIDIA CUDA Multi-Process Service (MPS) with NVIDIA Triton Inference Server on Amazon EC2 GPU instances cuts GPU infrastructure by 75% while holding sub-second latency at 92.1 requests per second per GPU.
AWS Machine Learning Blog / 4:24 PM
Preparing data for supervised fine-tuning Part 1: Formatting and quality
Data preparation determines the ceiling of any supervised fine-tuning project. This first post in a two-part series covers the foundations of SFT data prep: quality checks, conversational (JSONL) formatting, reasoning and tool-calling schemas, and a representative train/evaluation split.
AWS Machine Learning Blog / 4:57 PM
Accelerating aircraft IFEC diagnostics with agentic AI on AWS
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 while maintaining accuracy.
AWS Machine Learning Blog / 9:23 PM
Build a no-code ML workflow with Snowflake, Amazon SageMaker Canvas and Amazon Quick – Part 3: Visualizing insights with Amazon Quick Sight
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 stakeholders.
AWS Machine Learning Blog / 4:08 PM
Build an end-to-end RFI questionnaire workflow using Amazon Quick Automate
Learn how to build an end-to-end RFI questionnaire workflow with Amazon Quick Automate. Read a multi-tab RFI workbook from Amazon S3, use natural-language prompts to extract and structure the questionnaire data, refine the workflow through conversation, and write clean CSV output back to Amazon S3 — cutting development from days to hours.
AWS Machine Learning Blog / 3:55 PM
Agent Evaluation Metric for multi-turn conversations
Multi-turn agents fail in ways single-turn evaluation misses: one early mistake corrupts every later turn. This post introduces the Agent Evaluation Metric (AEM), a decomposable, turn-level way to measure agent quality, applied to its first dimension, correctness, to pinpoint the turn that caused a failure and separate it from the turns that inherited it.
AWS Machine Learning Blog / 10:26 PM
Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM
Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding.
AWS Machine Learning Blog / 8:01 PM
ICYMI: What landed for AI builders in August 2026
A recap of August 2026 launches for AI builders across Amazon Bedrock, Amazon Bedrock AgentCore, and Strands: million-token context for OpenAI models, cross-Region inference, agents that run for up to 14 days on dedicated compute, expanded AWS GovCloud availability, and Strands Robots for physical deployment.
AWS Machine Learning Blog / 6:11 PM
How Heurist Finance built an AI-native investment workbench on Amazon Bedrock AgentCore
Learn how Heurist built Heurist Finance, a conversational AI investment workbench, on Amazon Bedrock AgentCore. This customer story shows how AgentCore payments, Identity, Memory, Code Interpreter, and Observability let a small team buy premium market data per query, isolate analysis in a sandbox, and keep every action auditable.
AWS Machine Learning Blog / 3:51 PM
Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers
TorchServe is no longer maintained, leaving teams to own the entire GPU inference stack. The AWS Ray Serve Deep Learning Container is a supported, pre-tested container with the framework, GPU drivers, and serving layer already assembled. This post walks through deploying a vision-language model on Amazon EKS using the Ray Serve DLC on a single GPU node.
AWS Machine Learning Blog / 3:45 PM
Automate user-level custom permissions for Amazon Quick
Amazon Quick custom permissions let you enforce least-privilege access by toggling features per user. This post walks through four patterns to automate custom permissions across the user lifecycle: a RegisterUser API parameter, account and role defaults, event-driven Amazon EventBridge and AWS Lambda automation, and a retroactive batch update script.
AWS Machine Learning Blog / 7:12 PM
Pathway’s brain-inspired architecture development on Amazon SageMaker HyperPod
Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway develops and scales BDH on Amazon SageMaker HyperPod, and how BDH-CQ set a new cost-efficiency mark on the ARC-AGI-1 benchmark.
AWS Machine Learning Blog / 6:29 PM
Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes
Amazon SageMaker Feature Store now supports feature-level writes. With the new UpdateRecord API, you can update one or more feature values in a single call without reading or rewriting the entire record. It is available for both the Standard (Amazon DynamoDB) and In-Memory (Amazon ElastiCache) online store tiers.
AWS Machine Learning Blog / 5:03 PM
Govern models with MLflow and Amazon SageMaker AI Model Registry sync: Part 1
Managed MLflow on Amazon SageMaker AI now syncs richer model metadata (training metrics, evaluation results, inference specs, and lineage) into the SageMaker AI Model Registry, with lifecycle stage promotion. Part 1 shows how to govern candidate models in a single account using IAM guardrails.
Latest story in this edition: 6:26 PM
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