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Machine Learning on Amazon EKS

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Moscow

Moscow

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Description

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
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