Deploy a Hugging Face (PyAnnote) speaker diarization model on Amazon SageMaker as an asynchronous endpoint

Favorite Speaker diarization, an essential process in audio analysis, segments an audio file based on speaker identity. This post delves into integrating Hugging Face’s PyAnnote for speaker diarization with Amazon SageMaker asynchronous endpoints. We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on

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Shared by AWS Machine Learning April 26, 2024

Building scalable, secure, and reliable RAG applications using Knowledge Bases for Amazon Bedrock

Favorite Generative artificial intelligence (AI) has gained significant momentum with organizations actively exploring its potential applications. As successful proof-of-concepts transition into production, organizations are increasingly in need of enterprise scalable solutions. However, to unlock the long-term success and viability of these AI-powered solutions, it is crucial to align them with

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Shared by AWS Machine Learning April 24, 2024

Significant new capabilities make it easier to use Amazon Bedrock to build and scale generative AI applications – and achieve impressive results

Favorite We introduced Amazon Bedrock to the world a little over a year ago, delivering an entirely new way to build generative artificial intelligence (AI) applications. With the broadest selection of first- and third-party foundation models (FMs) as well as user-friendly capabilities, Amazon Bedrock is the fastest and easiest way

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Shared by AWS Machine Learning April 24, 2024

The executive’s guide to generative AI for sustainability

Favorite Organizations are facing ever-increasing requirements for sustainability goals alongside environmental, social, and governance (ESG) practices. A Gartner, Inc. survey revealed that 87 percent of business leaders expect to increase their organization’s investment in sustainability over the next years. This post serves as a starting point for any executive seeking

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Shared by AWS Machine Learning April 23, 2024

Integrate HyperPod clusters with Active Directory for seamless multi-user login

Favorite Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. With SageMaker HyperPod, you can train FMs for weeks and months without disruption. Typically, HyperPod clusters are used by multiple users: machine learning

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Shared by AWS Machine Learning April 23, 2024