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Improving model performance with PyTorch/XLA 2.6

For developers who want to use the PyTorch deep learning framework with Cloud TPUs, the PyTorch/XLA Python package is key, offering developers a way to run their PyTorch models on Cloud TPUs with only a few minor code changes. It does so by leveraging OpenXLA, developed by Google, which gives developers the ability to define …

Compact Neural TTS Voices for Accessibility

Contemporary text-to-speech solutions for accessibility applications can typically be classified into two categories: (i) device-based statistical parametric speech synthesis (SPSS) or unit selection (USEL) and (ii) cloud-based neural TTS. SPSS and USEL offer low latency and low disk footprint at the expense of naturalness and audio quality. Cloud-based neural TTS systems provide significantly better audio …

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DeepSeek-R1 model now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart

Today, we are announcing that DeepSeek AI’s first-generation frontier model, DeepSeek-R1, is available through Amazon SageMaker JumpStart and Amazon Bedrock Marketplace to deploy for inference. You can now use DeepSeek-R1 to build, experiment, and responsibly scale your generative AI ideas on AWS. In this post, we demonstrate how to get started with DeepSeek-R1 on Amazon …

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Deploy DeepSeek-R1 Distilled Llama models in Amazon Bedrock

Open foundation models (FMs) have become a cornerstone of generative AI innovation, enabling organizations to build and customize AI applications while maintaining control over their costs and deployment strategies. By providing high-quality, openly available models, the AI community fosters rapid iteration, knowledge sharing, and cost-effective solutions that benefit both developers and end-users. DeepSeek AI, a …

Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models

Scaling the capacity of language models has consistently proven to be a reliable approach for improving performance and unlocking new capabilities. Capacity can be primarily defined by two dimensions: the number of model parameters and the compute per example. While scaling typically involves increasing both, the precise interplay between these factors and their combined contribution …

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Develop a RAG-based application using Amazon Aurora with Amazon Kendra

Generative AI and large language models (LLMs) are revolutionizing organizations across diverse sectors to enhance customer experience, which traditionally would take years to make progress. Every organization has data stored in data stores, either on premises or in cloud providers. You can embrace generative AI and enhance customer experience by converting your existing data into …

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Create a SageMaker inference endpoint with custom model & extended container

Amazon SageMaker provides a seamless experience for building, training, and deploying machine learning (ML) models at scale. Although SageMaker offers a wide range of built-in algorithms and pre-trained models through Amazon SageMaker JumpStart, there are scenarios where you might need to bring your own custom model or use specific software dependencies not available in SageMaker …