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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

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By harnessing the capabilities of generative AI, you can automate the generation of comprehensive metadata descriptions for your data assets based on their documentation, enhancing discoverability, understanding, and the overall data governance within your AWS Cloud environment.

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Using Amazon Q Business with AWS HealthScribe to gain insights from patient consultations

AWS Machine Learning Blog

With the advent of generative AI and machine learning, new opportunities for enhancement became available for different industries and processes. AWS HealthScribe combines speech recognition and generative AI trained specifically for healthcare documentation to accelerate clinical documentation and enhance the consultation experience.

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Implement RAG while meeting data residency requirements using AWS hybrid and edge services

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In this post, we show how to extend Amazon Bedrock Agents to hybrid and edge services such as AWS Outposts and AWS Local Zones to build distributed Retrieval Augmented Generation (RAG) applications with on-premises data for improved model outcomes.

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AWS Machine Learning: A Beginner’s Guide

How to Learn Machine Learning

If you’re diving into the world of machine learning, AWS Machine Learning provides a robust and accessible platform to turn your data science dreams into reality. Introduction Machine learning can seem overwhelming at first – from choosing the right algorithms to setting up infrastructure.

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Principal Financial Group uses QnABot on AWS and Amazon Q Business to enhance workforce productivity with generative AI

AWS Machine Learning Blog

Principal wanted to use existing internal FAQs, documentation, and unstructured data and build an intelligent chatbot that could provide quick access to the right information for different roles. Principal also used the AWS open source repository Lex Web UI to build a frontend chat interface with Principal branding.

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Amazon Q Business simplifies integration of enterprise knowledge bases at scale

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Large-scale data ingestion is crucial for applications such as document analysis, summarization, research, and knowledge management. These tasks often involve processing vast amounts of documents, which can be time-consuming and labor-intensive. Then we introduce the solution deployment using three AWS CloudFormation templates.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning Blog

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. You can use AWS services such as Application Load Balancer to implement this approach. API Gateway also provides a WebSocket API. These components are illustrated in the following diagram.

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