Posted On: May 16, 2022
Today, we are pleased to announce the general availability of AWS support for Kubeflow v1.4. Kubeflow on AWS streamlines data science tasks and helps build highly reliable, secure, portable, and scalable ML systems with reduced operational overheads through integrations with AWS managed services. You can use this Kubeflow distribution to build ML systems on top of Amazon Elastic Kubernetes Service (Amazon EKS) to build, train, tune, and deploy ML models for a wide variety of use cases, including computer vision, natural language processing, speech translation, and financial modeling.
Kubeflow on AWS provides a clear path to use Kubeflow with Amazon EKS for managed Kubernetes clusters, Amazon Simple Storage Service (Amazon S3) for an easy-to-use pipeline artifacts store, Amazon Relational Database Service (Amazon RDS) for highly scalable pipelines and metadata store, Amazon Elastic File System/Amazon FSx for Lustre for a simple, scalable and serverless file storage solution for increased training performance, AWS Secrets Manager to protect secrets needed to access your applications, AWS CloudWatch for persistent log management, AWS Deep Learning Containers for highly optimized Jupyter notebook server images, AWS Application Load Balancer for secure external traffic management over HTTPS, AWS Cognito for user authentication with TLS. These AWS service integrations with Kubeflow allow you to decouple critical parts of the Kubeflow control plane from Kubernetes providing secure, scalable, resilient and cost optimized design.
To get started, please refer to the links listed below.
- Check out the Kubeflow on AWS blog post
- Refer to the Kubeflow on AWS website for details on all available Kubeflow deployment options
- Follow AWS Labs repository to track all AWS contributions to Kubeflow
- To learn more about the new features and getting started with new capabilities, please refer to the documentation on Kubeflow.org website