AI

How INRIX accelerates transportation planning with Amazon Bedrock

This post is co-written with Shashank Saraogi, Nat Gale, and Durran Kelly from INRIX. The complexity of modern traffic management extends far beyond mere road monitoring, encompassing massive amounts of data collected worldwide from connected cars, mobile devices, roadway sensors, and major event monitoring systems. For transportation authorities managing urban, suburban, and rural traffic flow, […]

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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

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

Software as a service (SaaS) companies managing multiple tenants face a critical challenge: efficiently extracting meaningful insights from vast document collections while controlling costs. Traditional approaches often lead to unnecessary spending on unused storage and processing resources, impacting both operational efficiency and profitability. Organizations need solutions that intelligently scale processing and storage resources based on

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

Organizations deploying video monitoring systems face a critical challenge: processing continuous video streams while maintaining accurate situational awareness. Traditional monitoring approaches that use rule-based detection or basic computer vision frequently miss important events or generate excessive false positives, leading to operational inefficiencies and alert fatigue. In this post, we show how to build a fully

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New postdoctoral fellowship program to accelerate innovation in health care

The MIT Health and Life Sciences Collaborative (MIT HEALS) is launching the Biswas Postdoctoral Fellowship Program to advance the work of outstanding early-career researchers in health and life sciences. Supported by a gift from the Biswas Family Foundation, the program aims to help apply cutting-edge research to improve health care and the lives of millions.

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Exploring data and its influence on political behavior

Data and politics are becoming increasingly intertwined. Today’s political campaigns and voter mobilization efforts are now entirely data-driven. Voters, pollsters, and elected officials are relying on data to make choices that have local, regional, and national impacts. A Department of Political Science course offers students tools to help make sense of these choices and their

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Robotic probe quickly measures key properties of new materials

Scientists are striving to discover new semiconductor materials that could boost the efficiency of solar cells and other electronics. But the pace of innovation is bottlenecked by the speed at which researchers can manually measure important material properties. A fully autonomous robotic system developed by MIT researchers could speed things up. Their system utilizes a

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

In the telecommunications industry, managing complex network infrastructures requires processing vast amounts of data from multiple sources. Network engineers often spend considerable time manually gathering and analyzing this data, taking away valuable hours that could be spent on strategic initiatives. This challenge led Swisscom, Switzerland’s leading telecommunications provider, to explore how AI can transform their

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

Although rapid generative AI advancements are revolutionizing organizational natural language processing tasks, developers and data scientists face significant challenges customizing these large models. These hurdles include managing complex workflows, efficiently preparing large datasets for fine-tuning, implementing sophisticated fine-tuning techniques while optimizing computational resources, consistently tracking model performance, and achieving reliable, scalable deployment.The fragmented nature of

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