Broadcast Date: September 27, 2021
Level: 300
Are you looking to learn how to deploy your ML workflows in the cloud using Kubernetes? In this tech talk, we’ll take you on a MLOps journey so you can discover how to train and host ML models at scale using AWS Deep Learning Containers (DLCs). DLCs provide a set of regularly updated libraries and packages to train and serve models in TensorFlow, PyTorch, or MXNet. You will also learn about the benefits of using DLCs with Amazon Elastic Kubernetes Service (EKS) and learn how to get started with production training and inference workflows.
Learning Objectives
- Learn about AWS Deep Learning Containers (DLCs)
- Learn how you can customize your MLOps workflow with Deep Learning containers
- Get started with AWS DLCs to train and host models on Amazon Elastic Kubernetes Service (EKS)
Who Should Attend?
MLOps, ML Practitioners, Data Scientists
Speakers
- Arjuna Keshavan, Software Dev Engineer, AWS
- Naina Prasad, Technical Program Manager, AWS
Learn More
To learn more about the services featured in this talk, please visit:
https://aws.amazon.com/machine-learning/containers/
Intro body copy here about 2018 re:Invent launches.
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Compute
Service How To
December 19th, 2018 | 1:00 PM PT
Developing Deep Learning Models for Computer Vision with
Amazon EC2 P3 Instances.
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Webinar 1:
What's New / Cloud Innovation
December 10th, 2018 | 11:00 AM PT
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