Broadcast Date: July 31, 2019
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.
- 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
Service How To
December 19th, 2018 | 1:00 PM PT
Developing Deep Learning Models for Computer Vision with
Amazon EC2 P3 Instances.