At a glance
Overview
See how you can connect custom large language models (LLMs) to enterprise data to deliver accurate responses for your artificial intelligence (AI) applications with NVIDIA on Amazon Web Services (AWS).
Date & time
Overview
Overview
You will learn how to
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Connect LLMs to multiple data sources and knowledge bases so that users can easily interact with data and receive accurate, up-to-date answers.
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Quickly train, customize, and deploy LLMs at scale leveraging existing code and pretrained models.
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Accelerate time to solution and reduce total cost of ownership (TCO) for deploying AI into production with NVIDIA AI Enterprise on AWS.
Overview
Speakers
Brian Kreitzer
Sr. Partner Solutions Architect, AWS
Charlie Huang
Sr. Product Marketing Manager - Enterprise Computing, NVIDIA
Peccy
Peculiar Ways, Amazon
Peccy
Peculiar Ways, Amazon
Peccy
Peculiar Ways, Amazon
Peccy
Peculiar Ways, Amazon
Peccy
Peculiar Ways, Amazon
Peccy
Peculiar Ways, Amazon
Details
Description
LLMs can help increase efficiency across enterprise tasks but these models often struggle to understand real-time events and specific knowledge domains, which can lead to inaccuracies. Fine-tuning LLMs can help improve accuracy, but it can be costly, labor-intensive, and require ample technical expertise. NeMo Retriever, part of NVIDIA AI Enterprise, on AWS helps enterprises enhance their generative AI applications with retrieval-augmented generation (RAG) capabilities.
Join this Spotlight Series event to see a demo on how NeMo Retriever can optimize the embedding and retrieval process of RAG to deliver higher accuracy and more efficient responses for AI applications.
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