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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! Our boss, Rick Fish, represented our team.

AWS 100
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Meta’s open AI hardware vision

Hacker News

Over the course of 2023, we rapidly scaled up our training clusters from 1K, 2K, 4K, to eventually 16K GPUs to support our AI workloads. Today, we’re training our models on two 24K-GPU clusters. We don’t expect this upward trajectory for AI clusters to slow down any time soon. Building AI clusters requires more than just GPUs.

AI 180
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How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

AWS Machine Learning Blog

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

AWS 98
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Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

AWS Machine Learning Blog

Amazon SageMaker distributed training jobs enable you with one click (or one API call) to set up a distributed compute cluster, train a model, save the result to Amazon Simple Storage Service (Amazon S3), and shut down the cluster when complete. Finally, launching clusters can introduce operational overhead due to longer starting time.

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Identifying defense coverage schemes in NFL’s Next Gen Stats

AWS Machine Learning Blog

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. In this post, we deep dive into the technical details of this ML model.

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23 Best Free NLP Datasets for Machine Learning

Iguazio

20 Newsgroups A dataset containing roughly 20,000 newsgroup documents spanning a variety of topics, for text classification, text clustering and similar ML applications. million articles from 20,000 news sources across a seven day period in 2017 and 2018. Get the dataset here. Long-Form Content 14. Get the dataset here.