AI news from Amazon Web Services – AWS

• Amazon
• news
• newsfeed
• artificial intelligence

View books and computing supplies on the AI industry from Amazon

Tweets by awscloud

1. How INRIX accelerates transportation planning with Amazon Bedrock

   INRIX pioneered the use of GPS data from connected vehicles for transportation intelligence. In this post, we partnered with Amazon Web Services (AWS) customer INRIX to demonstrate how Amazon Bedrock can be used to determine the best countermeasures for specific city locations using rich transportation data and how such countermeasures can be automatically visualized in street view images. This...

2. Qwen3 family of reasoning models now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

   Today, we are excited to announce that Qwen3, the latest generation of large language models (LLMs) in the Qwen family, is available through Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can deploy the Qwen3 models—available in 0.6B, 4B, 8B, and 32B parameter sizes—to build, experiment, and responsibly scale your generative AI applications on AWS. In this post,...

3. Build a just-in-time knowledge base with Amazon Bedrock

   Traditional Retrieval Augmented Generation (RAG) systems consume valuable resources by ingesting and maintaining embeddings for documents that might never be queried, resulting in unnecessary storage costs and reduced system efficiency. This post presents a just-in-time knowledge base solution that reduces unused consumption through intelligent document processing. The solution processes...

4. Agents as escalators: Real-time AI video monitoring with Amazon Bedrock Agents and video streams

   In this post, we show how to build a fully deployable solution that processes video streams using OpenCV, Amazon Bedrock for contextual scene understanding and automated responses through Amazon Bedrock Agents. This solution extends the capabilities demonstrated in Automate chatbot for document and data retrieval using Amazon Bedrock Agents and Knowledge Bases, which discussed using Amazon...

5. Transforming network operations with AI: How Swisscom built a network assistant using Amazon Bedrock

   In this post, we explore how Swisscom developed their Network Assistant. We discuss the initial challenges and how they implemented a solution that delivers measurable benefits. We examine the technical architecture, discuss key learnings, and look at future enhancements that can further transform network operations.

6. End-to-End model training and deployment with Amazon SageMaker Unified Studio

   In this post, we guide you through the stages of customizing large language models (LLMs) with SageMaker Unified Studio and SageMaker AI, covering the end-to-end process starting from data discovery to fine-tuning FMs with SageMaker AI distributed training, tracking metrics using MLflow, and then deploying models using SageMaker AI inference for real-time inference. We also discuss best...

7. Optimize RAG in production environments using Amazon SageMaker JumpStart and Amazon OpenSearch Service

   In this post, we show how to use Amazon OpenSearch Service as a vector store to build an efficient RAG application.

8. Advancing AI agent governance with Boomi and AWS: A unified approach to observability and compliance

   In this post, we share how Boomi partnered with AWS to help enterprises accelerate and scale AI adoption with confidence using Agent Control Tower.

9. Use Amazon SageMaker Unified Studio to build complex AI workflows using Amazon Bedrock Flows

   In this post, we demonstrate how you can use SageMaker Unified Studio to create complex AI workflows using Amazon Bedrock Flows.

10. Accelerating AI innovation: Scale MCP servers for enterprise workloads with Amazon Bedrock

    In this post, we present a centralized Model Context Protocol (MCP) server implementation using Amazon Bedrock that provides shared access to tools and resources for enterprise AI workloads. The solution enables organizations to accelerate AI innovation by standardizing access to resources and tools through MCP, while maintaining security and governance through a centralized approach.