Remove Auto-complete Remove Large Language Models Remove Metadata
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Evaluate large language models for your machine translation tasks on AWS

AWS Machine Learning Blog

Large language models (LLMs) have demonstrated promising capabilities in machine translation (MT) tasks. Depending on the use case, they are able to compete with neural translation models such as Amazon Translate. When using the FAISS adapter, translation units are stored into a local FAISS index along with the metadata.

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Multimodal Large Language Models

The MLOps Blog

TL;DR Multimodal Large Language Models (MLLMs) process data from different modalities like text, audio, image, and video. Compared to text-only models, MLLMs achieve richer contextual understanding and can integrate information across modalities, unlocking new areas of application.

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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.

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Build a serverless meeting summarization backend with large language models on Amazon SageMaker JumpStart

AWS Machine Learning Blog

The performance and quality of the models also improved drastically with the number of parameters. These models span tasks like text-to-text, text-to-image, text-to-embedding, and more. You can use large language models (LLMs), more specifically, for tasks including summarization, metadata extraction, and question answering.

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Training large language models on Amazon SageMaker: Best practices

AWS Machine Learning Blog

Language models are statistical methods predicting the succession of tokens in sequences, using natural text. Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical.

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

AWS Machine Learning Blog

Since 2018, using state-of-the-art proprietary and open source large language models (LLMs), our flagship product— Rad AI Impressions — has significantly reduced the time radiologists spend dictating reports, by generating Impression sections. 3 seconds, with minimal latency.

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How Veritone uses Amazon Bedrock, Amazon Rekognition, Amazon Transcribe, and information retrieval to update their video search pipeline

AWS Machine Learning Blog

Veritone’s current media search and retrieval system relies on keyword matching of metadata generated from ML services, including information related to faces, sentiment, and objects. With recent advances in large language models (LLMs), Veritone has updated its platform with these powerful new AI capabilities.

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