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Node problem detection and recovery for AWS Neuron nodes within Amazon EKS clusters

AWS Machine Learning Blog

In the post, we introduce the AWS Neuron node problem detector and recovery DaemonSet for AWS Trainium and AWS Inferentia on Amazon Elastic Kubernetes Service (Amazon EKS). Additionally, the node recovery agent will publish Amazon CloudWatch metrics for users to monitor and alert on these events. install.sh install.sh

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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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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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CBRE and AWS perform natural language queries of structured data using Amazon Bedrock

AWS Machine Learning Blog

Because Amazon Bedrock is serverless, you don’t have to manage infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with. AWS Prototyping developed an AWS Cloud Development Kit (AWS CDK) stack for deployment following AWS best practices.

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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

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By using dbt Cloud for data transformation, data teams can focus on writing business rules to drive insights from their transaction data to respond effectively to critical, time sensitive events. Solution overview Let’s consider TICKIT , a fictional website where users buy and sell tickets online for sporting events, shows, and concerts.

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Introducing Amazon SageMaker HyperPod to train foundation models at scale

AWS Machine Learning Blog

Building foundation models (FMs) requires building, maintaining, and optimizing large clusters to train models with tens to hundreds of billions of parameters on vast amounts of data. SageMaker HyperPod integrates the Slurm Workload Manager for cluster and training job orchestration.

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Federated learning on AWS using FedML, Amazon EKS, and Amazon SageMaker

AWS Machine Learning Blog

Therefore, ML creates challenges for AWS customers who need to ensure privacy and security across distributed entities without compromising patient outcomes. Solution overview We deploy FedML into multiple EKS clusters integrated with SageMaker for experiment tracking. As always, AWS welcomes your feedback.

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