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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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Building Generative AI and ML solutions faster with AI apps from AWS partners using Amazon SageMaker

AWS Machine Learning Blog

Our customers want a simple and secure way to find the best applications, integrate the selected applications into their machine learning (ML) and generative AI development environment, manage and scale their AI projects. Comet has been trusted by enterprise customers and academic teams since 2017.

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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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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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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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How Marubeni is optimizing market decisions using AWS machine learning and analytics

AWS Machine Learning Blog

MPII is using a machine learning (ML) bid optimization engine to inform upstream decision-making processes in power asset management and trading. MPII’s bid optimization engine solution uses ML models to generate optimal bids for participation in different markets. Data comes from disparate sources in a number of formats.

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