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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning Blog

The excitement is building for the fourteenth edition of AWS re:Invent, and as always, Las Vegas is set to host this spectacular event. Third, we’ll explore the robust infrastructure services from AWS powering AI innovation, featuring Amazon SageMaker , AWS Trainium , and AWS Inferentia under AI/ML, as well as Compute topics.

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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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Deploy Meta Llama 3.1-8B on AWS Inferentia using Amazon EKS and vLLM

AWS Machine Learning Blog

AWS Trainium and AWS Inferentia based instances, combined with Amazon Elastic Kubernetes Service (Amazon EKS), provide a performant and low cost framework to run LLMs efficiently in a containerized environment. Solution overview The steps to implement the solution are as follows: Create the EKS cluster.

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PEFT fine tuning of Llama 3 on SageMaker HyperPod with AWS Trainium

AWS Machine Learning Blog

The process of setting up and configuring a distributed training environment can be complex, requiring expertise in server management, cluster configuration, networking and distributed computing. To simplify infrastructure setup and accelerate distributed training, AWS introduced Amazon SageMaker HyperPod in late 2023.

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Map Earth’s vegetation in under 20 minutes with Amazon SageMaker

AWS Machine Learning Blog

Amazon SageMaker supports geospatial machine learning (ML) capabilities, allowing data scientists and ML engineers to build, train, and deploy ML models using geospatial data. We use the purpose-built geospatial container with SageMaker Processing jobs for a simplified, managed experience to create and run a cluster.

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ByteDance processes billions of daily videos using their multimodal video understanding models on AWS Inferentia2

AWS Machine Learning Blog

These experiences are made possible by our machine learning (ML) backend engine, with ML models built for video understanding, search, recommendation, advertising, and novel visual effects. By using sophisticated ML algorithms, the platform efficiently scans billions of videos each day.

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Enhance your Amazon Redshift cloud data warehouse with easier, simpler, and faster machine learning using Amazon SageMaker Canvas

AWS Machine Learning Blog

Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. For this post we’ll use a provisioned Amazon Redshift cluster.