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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. Each table represents a single data store.

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Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector database on AWS Marketplace

AWS Machine Learning Blog

Despite their wealth of general knowledge, state-of-the-art LLMs only have access to the information they were trained on. This can lead to factual inaccuracies (hallucinations) when the LLM is prompted to generate text based on information they didn’t see during their training.

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Super charge your LLMs with RAG at scale using AWS Glue for Apache Spark

AWS Machine Learning Blog

RAG introduces an information retrieval component that uses the user input to first pull information from a new data source. The data might exist in various formats such as files, database records, or long-form text. This new data from outside of the LLM’s original training data set is called external data.

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Advance environmental sustainability in clinical trials using AWS

AWS Machine Learning Blog

AWS can play a key role in enabling fast implementation of these decentralized clinical trials. By exploring these AWS powered alternatives, we aim to demonstrate how organizations can drive progress towards more environmentally friendly clinical research practices.

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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning Blog

A reverse image search engine enables users to upload an image to find related information instead of using text-based queries. It works by analyzing the visual content to find similar images in its database. The AWS Command Line Interface (AWS CLI) installed on your machine to upload the dataset to Amazon S3.

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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. Some applications may need to access data with personal identifiable information (PII) while others may rely on noncritical data.

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How generative AI is transforming legal tech with AWS

AWS Machine Learning Blog

Legal tech professionals, like any other business handling sensitive customer information, require robust security and confidentiality practices. AWS AI and machine learning (ML) services help address these concerns within the industry. These capabilities are built using the AWS Cloud. At AWS, security is our top priority.

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