AI

Derive meaningful and actionable operational insights from AWS Using Amazon Q Business

As a customer, you rely on Amazon Web Services (AWS) expertise to be available and understand your specific environment and operations. Today, you might implement manual processes to summarize lessons learned, obtain recommendations, or expedite the resolution of an incident. This can be time consuming, inconsistent, and not readily accessible. This post shows how to […]

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AI method radically speeds predictions of materials’ thermal properties

It is estimated that about 70 percent of the energy generated worldwide ends up as waste heat. If scientists could better predict how heat moves through semiconductors and insulators, they could design more efficient power generation systems. However, the thermal properties of materials can be exceedingly difficult to model. The trouble comes from phonons, which

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Accelerate your generative AI distributed training workloads with the NVIDIA NeMo Framework on Amazon EKS

In today’s rapidly evolving landscape of artificial intelligence (AI), training large language models (LLMs) poses significant challenges. These models often require enormous computational resources and sophisticated infrastructure to handle the vast amounts of data and complex algorithms involved. Without a structured framework, the process can become prohibitively time-consuming, costly, and complex. Enterprises struggle with managing

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Governing the ML lifecycle at scale, Part 2: Multi-account foundations

Your multi-account strategy is the core of your foundational environment on AWS. Design decisions around your multi-account environment are critical for operating securely at scale. Grouping your workloads strategically into multiple AWS accounts enables you to apply different controls across workloads, track cost and usage, reduce the impact of account limits, and mitigate the complexity

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How to assess a general-purpose AI model’s reliability before it’s deployed

Foundation models are massive deep-learning models that have been pretrained on an enormous amount of general-purpose, unlabeled data. They can be applied to a variety of tasks, like generating images or answering customer questions. But these models, which serve as the backbone for powerful artificial intelligence tools like ChatGPT and DALL-E, can offer up incorrect

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Video auto-dubbing using Amazon Translate, Amazon Bedrock, and Amazon Polly

This post is co-written with MagellanTV and Mission Cloud.  Video dubbing, or content localization, is the process of replacing the original spoken language in a video with another language while synchronizing audio and video. Video dubbing has emerged as a key tool in breaking down linguistic barriers, enhancing viewer engagement, and expanding market reach. However,

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