Remove Auto-complete Remove Generative AI Remove Metadata
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Announcing general availability of Amazon Bedrock Knowledge Bases GraphRAG with Amazon Neptune Analytics

AWS Machine Learning Blog

Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics.

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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

Enterprises may want to add custom metadata like document types (W-2 forms or paystubs), various entity types such as names, organization, and address, in addition to the standard metadata like file type, date created, or size to extend the intelligent search while ingesting the documents.

Metadata 118
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Evaluate large language models for your machine translation tasks on AWS

AWS Machine Learning Blog

When using the FAISS adapter, translation units are stored into a local FAISS index along with the metadata. The request is sent to the prompt generator. Also note the completion metrics on the left pane, displaying latency, input/output tokens, and quality scores. Cohere Embed supports 108 languages. Rerun the translation.

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

By surrounding unparalleled human expertise with proven technology, data and AI tools, Octus unlocks powerful truths that fuel decisive action across financial markets. Visit octus.com to learn how we deliver rigorously verified intelligence at speed and create a complete picture for professionals across the entire credit lifecycle.

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Scale AI training and inference for drug discovery through Amazon EKS and Karpenter

AWS Machine Learning Blog

At the same time, our generative AI models automatically design molecules targeting improvement across numerous properties, searching millions of candidates, and requiring enormous throughput and medium latency. We wanted to build a scalable system to support AI training and inference.

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

AWS Machine Learning Blog

This data is used to enrich the generative AI prompt to deliver more context-specific and accurate responses without continuously retraining the FM, while also improving transparency and minimizing hallucinations. Prerequisites Complete the following prerequisite steps: Make sure you have model access in Amazon Bedrock.

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Rad AI reduces real-time inference latency by 50% using Amazon SageMaker

AWS Machine Learning Blog

For years, Rad AI has been a reliable partner to radiology practices and health systems, consistently delivering high availability and generating complete results seamlessly in 0.5–3 In this post, we share how Rad AI reduced real-time inference latency by 50% using Amazon SageMaker. 3 seconds, with minimal latency.