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Arm's Neoverse V2, in AWS's Graviton 4

Hacker News

Amazon Web Services (AWS) is the largest cloud provider, and an early Arm server adopter. AWS started investing into the Arm server ecosystem in 2018 with Graviton 1, which used 16 Cortex A72 cores. Three generations later, AWS's Graviton 4 packs 96 Neoverse V2 cores.

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Racing into the future: How AWS DeepRacer fueled my AI and ML journey

AWS Machine Learning Blog

In 2018, I sat in the audience at AWS re:Invent as Andy Jassy announced AWS DeepRacer —a fully autonomous 1/18th scale race car driven by reinforcement learning. But AWS DeepRacer instantly captured my interest with its promise that even inexperienced developers could get involved in AI and ML.

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Celebrating the final AWS DeepRacer League championship and road ahead

AWS Machine Learning Blog

The AWS DeepRacer League is the world’s first autonomous racing league, open to everyone and powered by machine learning (ML). AWS DeepRacer brings builders together from around the world, creating a community where you learn ML hands-on through friendly autonomous racing competitions.

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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning Blog

In this post, we walk through how to fine-tune Llama 2 on AWS Trainium , a purpose-built accelerator for LLM training, to reduce training times and costs. We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw.

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Build a medical imaging AI inference pipeline with MONAI Deploy on AWS

AWS Machine Learning Blog

AWS and NVIDIA have come together to make this vision a reality. AWS, NVIDIA, and other partners build applications and solutions to make healthcare more accessible, affordable, and efficient by accelerating cloud connectivity of enterprise imaging. AHI provides API access to ImageSet metadata and ImageFrames.

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Generative AI and multi-modal agents in AWS: The key to unlocking new value in financial markets

AWS Machine Learning Blog

Implementing a multi-modal agent with AWS consolidates key insights from diverse structured and unstructured data on a large scale. All this is achieved using AWS services, thereby increasing the financial analyst’s efficiency to analyze multi-modal financial data (text, speech, and tabular data) holistically.

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Incorporate offline and online human – machine workflows into your generative AI applications on AWS

AWS Machine Learning Blog

We present the solution and provide an example by simulating a case where the tier one AWS experts are notified to help customers using a chat-bot. We provide LangChain and AWS SDK code-snippets, architecture and discussions to guide you on this important topic. Here, we use the on-demand option.

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