AI

Build RAG-based generative AI applications in AWS using Amazon FSx for NetApp ONTAP with Amazon Bedrock

The post is co-written with Michael Shaul and Sasha Korman from NetApp. Generative artificial intelligence (AI) applications are commonly built using a technique called Retrieval Augmented Generation (RAG) that provides foundation models (FMs) access to additional data they didn’t have during training. This data is used to enrich the generative AI prompt to deliver more […]

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Preserve access and explore alternatives for Amazon Lookout for Equipment

Amazon Lookout for Equipment, the AWS machine learning (ML) service designed for industrial equipment predictive maintenance, will no longer be open to new customers effective October 17, 2024. Existing customers will be able to use the service (both using the AWS Management Console and API) as normal and AWS will continue to invest in security,

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CRISPR-Cas9 guide RNA efficiency prediction with efficiently tuned models in Amazon SageMaker

The clustered regularly interspaced short palindromic repeat (CRISPR) technology holds the promise to revolutionize gene editing technologies, which is transformative to the way we understand and treat diseases. This technique is based in a natural mechanism found in bacteria that allows a protein coupled to a single guide RNA (gRNA) strand to locate and make

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Improve RAG performance using Cohere Rerank

This post is co-written with Pradeep Prabhakaran from Cohere. Retrieval Augmented Generation (RAG) is a powerful technique that can help enterprises develop generative artificial intelligence (AI) apps that integrate real-time data and enable rich, interactive conversations using proprietary data. RAG allows these AI applications to tap into external, reliable sources of domain-specific knowledge, enriching the

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