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Fine-tune NVIDIA Nemotron 3 models with Amazon SageMaker AI serverless model customization
Amazon Web Services has introduced serverless customization capabilities on SageMaker AI to simplify the fine-tuning of NVIDIA's Nemotron 3 models.
AWS ML Blog
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Real-time dental image verification with Amazon SageMaker AI at Henry Schein One
Henry Schein One has deployed an AI-powered quality verification system on Amazon SageMaker to analyze dental X-rays in real time across thousands of clinics.
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Build a semantic layer for agentic AI on AWS with Stardog and Amazon Bedrock AgentCore
AWS detailed a method for building a semantic data layer using Stardog and Amazon Bedrock AgentCore to enable AI agents to query multiple databases without complex ETL processes.
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Scaling agentic workflows with native case management in Amazon Quick Automate
Amazon has integrated native case management features into Quick Automate to help developers orchestrate and track complex, multi-step AI agent workflows.
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Deploying quantized models on Amazon SageMaker AI with Unsloth
AWS has outlined four deployment patterns for running quantized machine learning models optimized by Unsloth on its SageMaker and cloud container infrastructure.
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How KTern.AI built agentic AI for SAP on Amazon Bedrock AgentCore
KTern.AI has transitioned its SaaS platform into an agentic AI system for SAP software by leveraging Amazon Bedrock AgentCore and the Strands Agents SDK.
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Disaggregated prefill and decode for LLM inference on SageMaker HyperPod
AWS has demonstrated how to implement disaggregated prefill and decode using vLLM on SageMaker HyperPod to optimize large language model inference.
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MCP tool design: Practical approaches and tradeoffs
Amazon Web Services has published a guide detailing practical context engineering approaches to optimize Model Context Protocol tool design.
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Enhancing enterprise inference on Amazon SageMaker HyperPod with data capture, Hugging Face, NVMe, and Route 53 integration
Amazon Web Services has introduced five new capabilities for SageMaker HyperPod to improve enterprise AI inference, including direct Hugging Face deployment and faster NVMe model loading.
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Introducing Claude apps gateway for AWS
AWS has launched the Claude apps gateway, a self-hosted control plane allowing organizations to manage access, costs, and policies for Claude Code and Claude Desktop.
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Powering scientific discovery: BYOKG and GraphRAG for intelligent pharmaceutical research
Amazon Web Services detailed how combining graph databases with generative AI through Graph-based Retrieval Augmented Generation can accelerate pharmaceutical research.
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Automatically sort and prioritize your mailboxes by using Amazon Bedrock
A new guide demonstrates how public sector organizations can utilize Amazon Bedrock to automate email sorting and prioritization.
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Building and connecting a production-ready ecommerce MCP server using Amazon Bedrock AgentCore and Mistral AI Studio
This technical guide explains how to build and deploy an e-commerce Model Context Protocol server using Amazon Bedrock AgentCore and Mistral AI Studio.
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Securing Amazon Bedrock AgentCore Runtime with AWS WAF
AWS outlined architectural patterns to secure the Amazon Bedrock AgentCore Runtime using AWS Web Application Firewall and Application Load Balancers.
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Manage AI applications on Mac with Jamf’s AI Governance and Amazon Bedrock
AWS and Jamf have integrated Jamf's AI Governance with Amazon Bedrock to help organizations manage and secure AI applications across macOS fleets.
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Enrich your datasets with business context: Migrating from legacy Topics to semantic datasets in Amazon Quick
AWS has provided a migration guide for transitioning from legacy Topics to semantic datasets to enrich data with business context in Amazon QuickSight.
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Data modeling best practices for Amazon Quick Sight multi-dataset relationships
Amazon QuickSight has introduced Multi-Dataset Relationships, allowing users to define logical connections between datasets and perform runtime joins.
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Data modeling patterns for Amazon Quick Sight multi-dataset relationships
This post outlines specific data modeling patterns, schemas, and SQL queries for implementing the new multi-dataset relationships in Amazon QuickSight.
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Multi-dataset Topic best practices for Amazon Quick Chat
AWS shared optimization strategies for data architects building multi-dataset Topics for natural-language exploration in QuickSight.
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Build a unified semantic layer across datasets with multi-dataset Topics in Amazon Quick
This guide demonstrates how to build a unified semantic layer using multi-dataset Topics in Amazon QuickSight to enable cross-dataset natural language queries.