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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. At the time, I knew little about AI or machine learning (ML). seconds, securing the 2018 AWS DeepRacer grand champion title!

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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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Rad AI reduces real-time inference latency by 50% using Amazon SageMaker

AWS Machine Learning Blog

Since 2018, using state-of-the-art proprietary and open source large language models (LLMs), our flagship product— Rad AI Impressions — has significantly reduced the time radiologists spend dictating reports, by generating Impression sections. Rad AI’s ML organization tackles this challenge on two fronts.

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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

Quantitative modeling and forecasting – Generative models can synthesize large volumes of financial data to train machine learning (ML) models for applications like stock price forecasting, portfolio optimization, risk modeling, and more. Multi-modal models that understand diverse data sources can provide more robust forecasts.

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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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Llama 4 family of models from Meta are now available in SageMaker JumpStart

AWS Machine Learning Blog

Virginia) AWS Region. Prerequisites To try the Llama 4 models in SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker AI. The example extracts and contextualizes the buildspec-1-10-2.yml

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

AWS Machine Learning Blog

RLHF is a technique that combines rewards and comparisons, with human feedback to pre-train or fine-tune a machine learning (ML) model. 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.

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