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Amazon Bedrock

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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Generating style item descriptions by refining a visual-language model using SageMaker and Amazon Bedrock.

Creating high-quality product descriptions for millions of products is a significant challenge in online retail. Machine learning (ML) and natural language processing (NLP) technologies can automate this task, vastly improving the searchability of products and increasing customer satisfaction. This article presents a method of predicting domain-specific product attributes from product images with a fine-tuned Vision-Language…

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Creating workflows for Generative AI prompt chaining with human involvement.

Generative AI is a type of artificial intelligence that utilizes machine learning models and large amounts of data to create new content such as conversations, images, videos, and music. Generative AI works through foundation models (FMs), large pre-trained models capable of performing a variety of tasks based on input prompts. Large Language Models (LLMs), a class…

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Construct generative AI programs using Amazon Titan Text Premier, Amazon Bedrock, and AWS CDK.

Amazon has launched Amazon Titan Text Premier, a new large language model (LLM) as part of its Titan Text models. The model is now available in Amazon Bedrock, a managed service that provides a selection of high-performing foundation models from leading AI companies. The new model is intended for large-scale enterprise-grade text generation applications. Amazon…

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