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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Amazon Bedrock provides a broad range of models from Amazon and third-party providers, including Anthropic, AI21, Meta, Cohere, and Stability AI, and covers a wide range of use cases, including text and image generation, embedding, chat, high-level agents with reasoning and orchestration, and more.

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The $400 billion opportunity for AI in customer service

CIO

She’s been replaced by a second-generation AI bot, Bo.) That could soon change thanks to the meteoric rise of generative AI, which promises to make bots’ chat more human by contextualizing customer requests and synthesizing natural-sounding language. With enterprise spending on generative AI projected to hit $1.3

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Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Generative AI agents are a versatile and powerful tool for large enterprises. These agents excel at automating a wide range of routine and repetitive tasks, such as data entry, customer support inquiries, and content generation. System integration – Agents make API calls to integrated company systems to run specific actions.

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Build a contextual chatbot application using Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

One way to enable more contextual conversations is by linking the chatbot to internal knowledge bases and information systems. Integrating proprietary enterprise data from internal knowledge bases enables chatbots to contextualize their responses to each user’s individual needs and interests.

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning - AI

These generative AI applications are not only used to automate existing business processes, but also have the ability to transform the experience for customers using these applications. LangChain is an open source Python library designed to build applications with LLMs.

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Information extraction with LLMs using Amazon SageMaker JumpStart

AWS Machine Learning - AI

Although much of the current excitement is around LLMs for generative AI tasks, many of the key use cases that you might want to solve have not fundamentally changed. This is often achieved through the inclusion of human review, because no automated approach is entirely foolproof. that is due by July 31st.

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Build a contextual chatbot for financial services using Amazon SageMaker JumpStart, Llama 2 and Amazon OpenSearch Serverless with Vector Engine

AWS Machine Learning - AI

The financial service (FinServ) industry has unique generative AI requirements related to domain-specific data, data security, regulatory controls, and industry compliance standards. RAG is a framework for improving the quality of text generation by combining an LLM with an information retrieval (IR) system.