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From innovation to impact: How AWS and NVIDIA enable real-world generative AI success

AWS Machine Learning

Consider Hippocratic AIs work to develop AI-powered clinical assistants to support healthcare teams as doctors, nurses, and other clinicians face unprecedented levels of burnout. They arent just building another chatbot; they are reimagining healthcare delivery at scale. times lower latency compared to other platforms.

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Intelligent healthcare forms analysis with Amazon Bedrock

AWS Machine Learning

Generative artificial intelligence (AI) provides an opportunity for improvements in healthcare by combining and analyzing structured and unstructured data across previously disconnected silos. Generative AI can help raise the bar on efficiency and effectiveness across the full scope of healthcare delivery.

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How healthcare payers and plans can empower members with generative AI

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a unified API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.

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How Aetion is using generative AI and Amazon Bedrock to translate scientific intent to results

AWS Machine Learning

For decision-makers in healthcare, it is critical to gain a comprehensive understanding of patient journeys and health outcomes over time. The company provides comprehensive solutions to healthcare and life science customers to rapidly and transparently transforms real-world data into real-world evidence.

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Track LLM model evaluation using Amazon SageMaker managed MLflow and FMEval

AWS Machine Learning

As LLMs take on more significant roles in areas like healthcare, education, and decision support, robust evaluation frameworks are vital for building trust and realizing the technologys potential while mitigating risks. Developers interested in using LLMs should prioritize a comprehensive evaluation process for several reasons.

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Reducing hallucinations in LLM agents with a verified semantic cache using Amazon Bedrock Knowledge Bases

AWS Machine Learning

For organizations deploying LLMs in production applicationsparticularly in critical domains such as healthcare, finance, or legal servicesthese residual hallucinations pose serious risks, potentially leading to misinformation, liability issues, and loss of user trust. User submits a question When is re:Invent happening this year?,