Amazon· Bedrock / Nova

Run interactive IDEs on Amazon EKS with SageMaker AI to power up your AI workflows

To power up AI workflows on Amazon Elastic Kubernetes Service (Amazon EKS) , data scientists need interactive IDEs like JupyterLab and Code Editor. Yet running those IDEs usually means leaving the cluster that hosts their pipelines, moving to a standalone JupyterHub deployment or a local laptop. That switch leaves them without the GPU nodes, shared storage, and AWS Identity and Access Management (IAM) roles their pipelines depend on. The Amazon SageMaker AI Spaces add-on for Amazon EKS closes that gap. It runs managed JupyterLab and Code Editor environments on the cluster that you already operate. Standing up a standalone JupyterHub environment with GPU access, storage, and authentication typically takes a platform team 3–5 days. With the add-on, a data scientist launches a fully configured Space in about 5 minutes. In this post, you install the SageMaker AI Spaces add-on on an Amazon EKS cluster. You set up the supporting add-ons and IAM roles, deploy the AWS Load Balancer Controller , request a TLS certificate, and create an AWS Key Management System (AWS KMS) encryption key. …

Announced

10 Aug 2026 16:34 UTC