AI

Identifying Interactions at Scale for LLMs

<!– –> Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze

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Can AI help predict which heart-failure patients will worsen within a year?

Characterized by weakened or damaged heart musculature, heart failure results in the gradual buildup of fluid in a patient’s lungs, legs, feet, and other parts of the body. The condition is chronic and incurable, often leading to arrhythmias or sudden cardiac arrest. For many centuries, bloodletting and leeches were the treatment of choice, famously practiced

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Improve operational visibility for inference workloads on Amazon Bedrock with new CloudWatch metrics for TTFT and Estimated Quota Consumption

As organizations scale their generative AI workloads on Amazon Bedrock, operational visibility into inference performance and resource consumption becomes critical. Teams running latency-sensitive applications must understand how quickly models begin generating responses. Teams managing high-throughput workloads must understand how their requests consume quota so they can avoid unexpected throttling. Until now, gaining this visibility required

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Secure AI agents with Policy in Amazon Bedrock AgentCore

Deploying AI agents safely in regulated industries is challenging. Without proper boundaries, agents that access sensitive data or execute transactions can pose significant security risks. Unlike traditional software, an AI agent chooses actions to achieve a goal by invoking tools, accessing data, and adapting its reasoning using data from its environment and users. This autonomy

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Multimodal embeddings at scale: AI data lake for media and entertainment workloads

This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon Nova models and Amazon OpenSearch Service. You will learn how to move beyond manual tagging and keyword-based searches to enable semantic search that captures the full richness of video content. We

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Fine-tuning NVIDIA Nemotron Speech ASR on Amazon EC2 for domain adaptation

This post is a collaboration between AWS, NVIDIA and Heidi.  Automatic speech recognition (ASR), often called speech-to-text (STT) is becoming increasingly critical across industries like healthcare, customer service, and media production. While pre-trained models offer strong capabilities for general speech, fine-tuning for specific domains and use cases can enhance accuracy and performance. In this post,

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