AI

PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium

Training large language models (LLMs) models has become a significant expense for businesses. For many use cases, companies are looking to use LLM foundation models (FM) with their domain-specific data. However, companies are discovering that performing full fine tuning for these models with their data isn’t cost effective. To reduce costs while continuing to use the […]

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Using transcription confidence scores to improve slot filling in Amazon Lex

When building voice-enabled chatbots with Amazon Lex, one of the biggest challenges is accurately capturing user speech input for slot values. For example, when a user needs to provide their account number or confirmation code, speech recognition accuracy becomes crucial. This is where transcription confidence scores come in to help ensure reliable slot filling. What

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Improving Retrieval Augmented Generation accuracy with GraphRAG

Customers need better accuracy to take generative AI applications into production. In a world where decisions are increasingly data-driven, the integrity and reliability of information are paramount. To address this, customers often begin by enhancing generative AI accuracy through vector-based retrieval systems and the Retrieval Augmented Generation (RAG) architectural pattern, which integrates dense embeddings to

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Ecologists find computer vision models’ blind spots in retrieving wildlife images

Try taking a picture of each of North America’s roughly 11,000 tree species, and you’ll have a mere fraction of the millions of photos within nature image datasets. These massive collections of snapshots — ranging from butterflies to humpback whales — are a great research tool for ecologists because they provide evidence of organisms’ unique behaviors, rare conditions,

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