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

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

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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Congratulations to the top MSRC 2025 Q2 security researchers!

Congratulations to all the researchers recognized in this quarter’s Microsoft Researcher Recognition Program leaderboard! Thank you to everyone for your hard work and continued partnership to secure customers. The top three researchers of the 2025 Q2 Security Researcher Leaderboard are wkai, Brad Schlintz (nmdhkr), and 0x140ce! Check out the full list of researchers recognized this

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

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

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

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

Generative AI has revolutionized customer interactions across industries by offering personalized, intuitive experiences powered by unprecedented access to information. This transformation is further enhanced by Retrieval Augmented Generation (RAG), a technique that allows large language models (LLMs) to reference external knowledge sources beyond their training data. RAG has gained popularity for its ability to improve

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

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

Just as APIs became the standard for integration, AI agents are transforming workflow automation through intelligent task coordination. AI agents are already enhancing decision-making and streamlining operations across enterprises. But as adoption accelerates, organizations face growing complexity in managing them at scale. Organizations struggle with observability and lifecycle management, finding it difficult to monitor performance

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

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

Organizations face the challenge to manage data, multiple artificial intelligence and machine learning (AI/ML) tools, and workflows across different environments, impacting productivity and governance. A unified development environment consolidates data processing, model development, and AI application deployment into a single system. This integration streamlines workflows, enhances collaboration, and accelerates AI solution development from concept to

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