AI

Enhance Geospatial Analysis and GIS Workflows with Amazon Bedrock Capabilities

As data becomes more abundant and information systems grow in complexity, stakeholders need solutions that reveal quality insights. Applying emerging technologies to the geospatial domain offers a unique opportunity to create transformative user experiences and intuitive workstreams for users and organizations to deliver on their missions and responsibilities. In this post, we explore how you

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Beyond the basics: A comprehensive foundation model selection framework for generative AI

Most organizations evaluating foundation models limit their analysis to three primary dimensions: accuracy, latency, and cost. While these metrics provide a useful starting point, they represent an oversimplification of the complex interplay of factors that determine real-world model performance. Foundation models have revolutionized how enterprises develop generative AI applications, offering unprecedented capabilities in understanding and

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Accelerate intelligent document processing with generative AI on AWS

Every day, organizations process millions of documents, including invoices, contracts, insurance claims, medical records, and financial statements. Despite the critical role these documents play, an estimated 80–90% of the data they contain is unstructured and largely untapped, hiding valuable insights that could transform business outcomes. Despite advances in technology, many organizations still rely on manual

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Amazon SageMaker HyperPod enhances ML infrastructure with scalability and customizability

Amazon SageMaker HyperPod is a purpose-built infrastructure for optimizing foundation model (FM) training and inference at scale. SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training FMs, reducing training time by up to 40%. SageMaker HyperPod offers persistent clusters with built-in resiliency, while also offering deep

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Fine-tune OpenAI GPT-OSS models using Amazon SageMaker HyperPod recipes

This post is the second part of the GPT-OSS series focusing on model customization with Amazon SageMaker AI. In Part 1, we demonstrated fine-tuning GPT-OSS models using open source Hugging Face libraries with SageMaker training jobs, which supports distributed multi-GPU and multi-node configurations, so you can spin up high-performance clusters on demand. In this post,

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