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AWS empowers sales teams using generative AI solution built on Amazon Bedrock

AWS Machine Learning Blog

At AWS, we are transforming our seller and customer journeys by using generative artificial intelligence (AI) across the sales lifecycle. Prospecting, opportunity progression, and customer engagement present exciting opportunities to utilize generative AI, using historical data, to drive efficiency and effectiveness.

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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning Blog

In the context of generative AI , significant progress has been made in developing multimodal embedding models that can embed various data modalities—such as text, image, video, and audio data—into a shared vector space. The AWS Command Line Interface (AWS CLI) installed on your machine to upload the dataset to Amazon S3.

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Build cost-effective RAG applications with Binary Embeddings in Amazon Titan Text Embeddings V2, Amazon OpenSearch Serverless, and Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Amazon Bedrock is a fully managed service that provides a single API to access and use various high-performing foundation models (FMs) from leading AI companies. Amazon Bedrock also offers a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.

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Build a secure enterprise application with Generative AI and RAG using Amazon SageMaker JumpStart

AWS Machine Learning Blog

Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. With the advent of these LLMs or FMs, customers can simply build Generative AI based applications for advertising, knowledge management, and customer support.

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Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

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Generative AI models have the potential to revolutionize enterprise operations, but businesses must carefully consider how to harness their power while overcoming challenges such as safeguarding data and ensuring the quality of AI-generated content. k-NN works by finding the k most similar vectors to a given vector.

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How Druva used Amazon Bedrock to address foundation model complexity when building Dru, Druva’s backup AI copilot

AWS Machine Learning Blog

Independent software vendors (ISVs) like Druva are integrating AI assistants into their user applications to make software more accessible. Dru , the Druva backup AI copilot, enables real-time interaction and personalized responses, with users engaging in a natural conversation with the software.

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Semantic image search for articles using Amazon Rekognition, Amazon SageMaker foundation models, and Amazon OpenSearch Service

AWS Machine Learning Blog

It also relies on the images in the repository being tagged correctly, which can also be automated (for a customer success story, refer to Aller Media Finds Success with KeyCore and AWS ). Using the k-nearest neighbors (k-NN) algorithm, you define how many images to return in your results.