AI

Build a generative AI image description application with Anthropic’s Claude 3.5 Sonnet on Amazon Bedrock and AWS CDK

Generating image descriptions is a common requirement for applications across many industries. One common use case is tagging images with descriptive metadata to improve discoverability within an organization’s content repositories. Ecommerce platforms also use automatically generated image descriptions to provide customers with additional product details. Descriptive image captions also improve accessibility for users with visual […]

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

Harnessing the power of big data has become increasingly critical for businesses looking to gain a competitive edge. From deriving insights to powering generative artificial intelligence (AI)-driven applications, the ability to efficiently process and analyze large datasets is a vital capability. However, managing the complex infrastructure required for big data workloads has traditionally been a

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Best practices for prompt engineering with Meta Llama 3 for Text-to-SQL use cases

With the rapid growth of generative artificial intelligence (AI), many AWS customers are looking to take advantage of publicly available foundation models (FMs) and technologies. This includes Meta Llama 3, Meta’s publicly available large language model (LLM). The partnership between Meta and Amazon signifies collective generative AI innovation, and Meta and Amazon are working together

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Implementing advanced prompt engineering with Amazon Bedrock

Despite the ability of generative artificial intelligence (AI) to mimic human behavior, it often requires detailed instructions to generate high-quality and relevant content. Prompt engineering is the process of crafting these inputs, called prompts, that guide foundation models (FMs) and large language models (LLMs) to produce desired outputs. Prompt templates can also be used as

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Study: Transparency is often lacking in datasets used to train large language models

In order to train more powerful large language models, researchers use vast dataset collections that blend diverse data from thousands of web sources. But as these datasets are combined and recombined into multiple collections, important information about their origins and restrictions on how they can be used are often lost or confounded in the shuffle.

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Accelerate Generative AI Inference with NVIDIA NIM Microservices on Amazon SageMaker

This post is co-written with Eliuth Triana, Abhishek Sawarkar, Jiahong Liu, Kshitiz Gupta, JR Morgan and Deepika Padmanabhan from NVIDIA.  At the 2024 NVIDIA GTC conference, we announced support for NVIDIA NIM Inference Microservices in Amazon SageMaker Inference. This integration allows you to deploy industry-leading large language models (LLMs) on SageMaker and optimize their performance and

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Celebrating the final AWS DeepRacer League championship and road ahead

The AWS DeepRacer League is the world’s first autonomous racing league, open to everyone and powered by machine learning (ML). AWS DeepRacer brings builders together from around the world, creating a community where you learn ML hands-on through friendly autonomous racing competitions. As we celebrate the achievements of over 560,000 participants from more than 150

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Provide a personalized experience for news readers using Amazon Personalize and Amazon Titan Text Embeddings on Amazon Bedrock

News publishers want to provide a personalized and informative experience to their readers, but the short shelf life of news articles can make this quite difficult. In news publishing, articles typically have peak readership within the same day of publication. Additionally, news publishers frequently publish new articles and want to show these articles to interested

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