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Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

AWS Machine Learning Blog

With generative AI on AWS, you can reinvent your applications, create entirely new customer experiences, and improve overall productivity. You can use this post as a reference to build secure enterprise applications in the Generative AI domain using AWS services. An Amazon Simple Storage Service (Amazon S3) bucket.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning Blog

Such data often lacks the specialized knowledge contained in internal documents available in modern businesses, which is typically needed to get accurate answers in domains such as pharmaceutical research, financial investigation, and customer support. For example, imagine that you are planning next year’s strategy of an investment company.

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Implement unified text and image search with a CLIP model using Amazon SageMaker and Amazon OpenSearch Service

AWS Machine Learning Blog

Implementing this unified image and text search application consists of two phases: k-NN reference index – In this phase, you pass a set of corpus documents or product images through a CLIP model to encode them into embeddings. You save those embeddings into a k-NN index in OpenSearch Service. bin/bash MODEL_NAME=RN50.pt

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Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

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The Retrieval-Augmented Generation (RAG) framework augments prompts with external data from multiple sources, such as document repositories, databases, or APIs, to make foundation models effective for domain-specific tasks. MongoDB Atlas Vector Search uses a technique called k-nearest neighbors (k-NN) to search for similar vectors.

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Semantic image search for articles using Amazon Rekognition, Amazon SageMaker foundation models, and Amazon OpenSearch Service

AWS Machine Learning Blog

It also relies on the images in the repository being tagged correctly, which can also be automated (for a customer success story, refer to Aller Media Finds Success with KeyCore and AWS ). In this post, we demonstrate how to use Amazon Rekognition , Amazon SageMaker JumpStart , and Amazon OpenSearch Service to solve this business problem.

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Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 2

AWS Machine Learning Blog

We used AWS services including Amazon Bedrock , Amazon SageMaker , and Amazon OpenSearch Serverless in this solution. In this series, we use the slide deck Train and deploy Stable Diffusion using AWS Trainium & AWS Inferentia from the AWS Summit in Toronto, June 2023 to demonstrate the solution.

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Talk to your slide deck using multimodal foundation models hosted on Amazon Bedrock and Amazon SageMaker – Part 1

AWS Machine Learning Blog

and AWS services including Amazon Bedrock and Amazon SageMaker to perform similar generative tasks on multimodal data. In this post, we use the slide deck titled Train and deploy Stable Diffusion using AWS Trainium & AWS Inferentia from the AWS Summit in Toronto, June 2023, to demonstrate the solution.

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