This Guidance helps customers who have on-premises restrictions or who have existing Kubernetes investments to use either Amazon Elastic Kubernetes Service (Amazon EKS) and Kubeflow or Amazon SageMaker to implement a hybrid, distributed machine learning (ML) training architecture. Kubernetes is a widely adopted system for automating infrastructure deployment, resource scaling, and management of containerized applications. The open-source community developed a layer on top of Kubernetes called Kubeflow, which aims to make the deployment of end-to-end ML workflows on Kubernetes simple, portable, and scalable. With the ability to choose between two approaches at runtime in this architecture, customers gain maximum control over their ML deployments. They can continue using open-source libraries in their deep learning training script and still make it compatible to run on both Kubernetes and SageMaker.

Architecture Diagram

Download the architecture diagram PDF 

Well-Architected Pillars

The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

Implementation Resources

The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.

AWS Machine Learning

Build flexible and scalable distributed training architectures using Kubeflow on AWS and Amazon SageMaker

This blog post demonstrates how Kubeflow on AWS (an AWS-specific distribution of Kubeflow) used with  AWS Deep Learning Containers and  Amazon EFS simplifies collaboration and provides flexibility in training deep learning models at scale on both  Amazon EKS and  Amazon SageMaker utilizing a hybrid architecture approach.


The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.

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