AI

Use RAG for video generation using Amazon Bedrock and Amazon Nova Reel

Generating high-quality custom videos remains a significant challenge, because video generation models are limited to their pre-trained knowledge. This limitation affects industries such as advertising, media production, education, and gaming, where customization and control of video generation is essential. To address this, we developed a Video Retrieval Augmented Generation (VRAG) multimodal pipeline that transforms structured […]

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Introducing V-RAG: revolutionizing AI-powered video production with Retrieval Augmented Generation

A key development in generative AI is AI-powered video generation. Before AI, creating dynamic video content required extensive resources, technical expertise, and significant manual effort. Today, AI models can generate videos from simple inputs, but organizations still face challenges like unpredictable results. This post introduces Video Retrieval-Augmented Generation (V-RAG), an approach to help improve video

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Enhanced metrics for Amazon SageMaker AI endpoints: deeper visibility for better performance

Running machine learning (ML) models in production requires more than just infrastructure resilience and scaling efficiency. You need nearly continuous visibility into performance and resource utilization. When latency increases, invocations fail, or resources become constrained, you need immediate insight to diagnose and resolve issues before they impact your customers. Until now, Amazon SageMaker AI provided

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Enforce data residency with Amazon Quick extensions for Microsoft Teams

Organizations with users in multiple geographies face data residency requirements such as General Data Protection Regulation (GDPR) in Europe, country-specific data sovereignty laws, and internal compliance policies. Amazon Quick with Microsoft 365 extensions supports Regional routing to meet these requirements. Amazon Quick supports multi-Region deployments so you can route users to AWS Region-specific Amazon Quick

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A better method for identifying overconfident large language models

Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer. But this method measures self-confidence, and even the most impressive LLM might be

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