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Mixed-input matrix multiplication performance optimizations

Favorite Posted by Manish Gupta, Staff Software Engineer, Google Research AI-driven technologies are weaving themselves into the fabric of our daily routines, with the potential to enhance our access to knowledge and boost our overall productivity. The backbone of these applications lies in large language models (LLMs). LLMs are memory-intensive

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Shared by Google AI Technology January 26, 2024

Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector database on AWS Marketplace

Favorite Generative AI solutions have the potential to transform businesses by boosting productivity and improving customer experiences, and using large language models (LLMs) with these solutions has become increasingly popular. Building proofs of concept is relatively straightforward because cutting-edge foundation models are available from specialized providers through a simple API

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Shared by AWS Machine Learning January 24, 2024

Exphormer: Scaling transformers for graph-structured data

Favorite Posted by Ameya Velingker, Research Scientist, Google Research, and Balaji Venkatachalam, Software Engineer, Google Graphs, in which objects and their relations are represented as nodes (or vertices) and edges (or links) between pairs of nodes, are ubiquitous in computing and machine learning (ML). For example, social networks, road networks,

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Shared by Google AI Technology January 23, 2024

Chrome is getting 3 new generative AI features

Favorite Three experimental generative AI features are coming to Chrome on Macs and Windows PCs to make browsing easier and more personalized. View Original Source (blog.google/technology/ai/) Here.

Build a vaccination verification solution using the Queries feature in Amazon Textract

Favorite Amazon Textract is a machine learning (ML) service that enables automatic extraction of text, handwriting, and data from scanned documents, surpassing traditional optical character recognition (OCR). It can identify, understand, and extract data from tables and forms with remarkable accuracy. Presently, several companies rely on manual extraction methods or

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Shared by AWS Machine Learning January 22, 2024

Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning

Favorite In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. Pre-trained language models (PLMs) are undergoing rapid commercial and enterprise adoption in the areas of productivity tools, customer service, search

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Shared by AWS Machine Learning January 19, 2024

Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

Favorite Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. Using AWS Trainium and Inferentia based instances, through SageMaker, can help users lower fine-tuning costs by up to 50%, and lower deployment costs by

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Shared by AWS Machine Learning January 18, 2024