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      August 31, 2019

      Saturday  11:00 AM

      523 East Mally
      San Antonio, Texas 78221

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      Machine Learning on Amazon EKS

      July 31 | 11:00 AM - 12:00 PM PT (2:00 PM - 3:00 PM ET) Level 300 | Use Case How To Kubernetes provides isolation, auto-scaling, load balancing, flexibility and GPU support. These features are critical to run computationally, data-intensive and hard to parallelize machine learning models. Declarative syntax of Kubernetes deployment descriptors make it easy for non-operationally focused engineers to easily train machine learning models on Kubernetes. In this tech talk, we will explain why and how Amazon EKS is well-suited for single and multi-node distributed training, training your models, and deploying your models in production. Specifically, we will show how to use KubeFlow and TensorFlow on Amazon EKS for your machine learning needs. We will also demonstrate how to setup machine learning pipelines, and visualization tools like TensorBoard for monitoring. We will also discuss distributed training using Horovod. Learning Objectives Learn why and how Amazon EKS is well-suited for single and multi-node distributed training Learn how to train and deploy your models in production See how to use KubeFlow and TensorFlow on Amazon EKS for your machine learning needs Who Should Attend? Developers, Architects, Infrastructure Engineers Speakers Arun Gupta, Principal OS Technologist, AWS

      Categories: Science

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