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Amazon Titan

Develop a comprehensive RAG solution utilizing Knowledge Databases for Amazon Bedrock and AWS CloudFormation.

Retrieval Augmented Generation (RAG) is a cutting-edge method for constructing question answering systems, blending retrieval and foundation model capabilities. This unique approach first draws relevant data from a large body of text, using a foundation model to forge an answer from the collated information. Setting up an RAG system entails several elements such as a…

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Improve image search experiences using Amazon Personalize, Amazon OpenSearch Service, and Amazon Titan Multimodal Embeddings within Amazon Bedrock.

Historically, image captioning and text-to-image search have fascinated machine-learning practitioners and businesses. The open-source models, Contrastive Language-Image Pre-training (CLIP) and Bootstrapping Language-Image Pre-training (BLIP), were the first to produce near-human results. Recently, multimodal models using generative models are being used to map text and images to the same embedding space for best results. Amazon has…

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Efficient document categorization employing the Amazon Titan Multimodal Embeddings Model.

Businesses can automate the processing of vast quantities of various document formats using intelligent document processing (IDP) solutions powered by AI. These solutions categorize and extract insights from documents, reducing costs and errors and allowing for scalability. A significant aspect of IDP systems is document categorization, which guides the next steps based on the document…

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Utilize Amazon Bedrock and AWS Step Functions to facilitate the automation of altering image backgrounds.

Many customers in sectors such as creative advertising, media and entertainment, ecommerce, and fashion often need to change the background in a large number of images, which usually involves a time-consuming manual editing process for each image. Amazon Bedrock, in conjunction with AWS Step Functions, makes it easy to automate the process of changing backgrounds…

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