Remove Artificial Inteligence Remove Generative AI Remove Knowledge Base Remove Machine Learning
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Harness the Power of Pinecone with Cloudera’s New Applied Machine Learning Prototype

Cloudera

And so we are thrilled to introduce our latest applied ML prototype (AMP) — a large language model (LLM) chatbot customized with website data using Meta’s Llama2 LLM and Pinecone’s vector database.

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ChatGPT, the rise of generative AI

CIO

Over the last few months, both business and technology worlds alike have been abuzz about ChatGPT, and more than a few leaders are wondering what this AI advancement means for their organizations. It’s only one example of generative AI. GPT stands for generative pre-trained transformer. What is ChatGPT?

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Build custom generative AI applications powered by Amazon Bedrock

AWS Machine Learning - AI

I explored how Bedrock enables customers to build a secure, compliant foundation for generative AI applications. Now I’d like to turn to a slightly more technical, but equally important differentiator for Bedrock—the multiple techniques that you can use to customize models and meet your specific business needs.

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Delight your customers with great conversational experiences via QnABot, a generative AI chatbot

AWS Machine Learning - AI

QnABot on AWS (an AWS Solution) now provides access to Amazon Bedrock foundational models (FMs) and Knowledge Bases for Amazon Bedrock , a fully managed end-to-end Retrieval Augmented Generation (RAG) workflow. In turn, customers can ask a variety of questions and receive accurate answers powered by generative AI.

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Build a self-service digital assistant using Amazon Lex and Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Generative artificial intelligence (AI)-powered chatbots play a crucial role in delivering human-like interactions by providing responses from a knowledge base without the involvement of live agents. The following diagram illustrates the solution architecture and workflow.

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Introducing guardrails in Knowledge Bases for Amazon Bedrock

AWS Machine Learning - AI

Knowledge Bases for Amazon Bedrock is a fully managed capability that helps you securely connect foundation models (FMs) in Amazon Bedrock to your company data using Retrieval Augmented Generation (RAG). In the following sections, we demonstrate how to create a knowledge base with guardrails.

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Build an end-to-end RAG solution using Knowledge Bases for Amazon Bedrock and AWS CloudFormation

AWS Machine Learning - AI

An end-to-end RAG solution involves several components, including a knowledge base, a retrieval system, and a generation system. Building and deploying these components can be complex and error-prone, especially when dealing with large-scale data and models. Choose Sync to initiate the data ingestion job.