Uploaded August 2026 | Updated September 2026, 2 weeks ago
Don't miss out! Join us at our next KubeCon + CloudNativeCon events in Shanghai, China (8-9 September, 2026) and Salt Lake City, United States (Nov 9–12, 2026). Connect with our current graduated, incubating, and sandbox projects as the community gathers to further the education and advancement of cloud native computing. Learn more at kubecon.io
Kubeflow 2026 State of the Union: Orchestrating the Full ML Lifecycle - Yash Pal, student; Anya Kramar & Valentina Rodriguez Sosa, Red Hat
Kubeflow has grown into a comprehensive ecosystem that powers end-to-end machine learning on Kubernetes.
In this session, we provide a holistic ""State of the Union"" update across all major working groups. We will walk the audience through the lifecycle of a modern model, demonstrating how the unified SDK and Notebook Workspaces simplify authoring.
We will then trace how these workloads transition into scalable Kubeflow Pipelines, undergo distributed training and hyperparameter tuning via Trainer v2 and Katib, and are finally cataloged in the Model Registry before being deployed via KServe.
We will conclude by outlining the upcoming project roadmap and providing updates on how we can improve core Kubeflow components or build integration bridges to adjacent OSS AI projects.
Don't miss out! Join us at our next KubeCon + CloudNativeCon events in Shanghai, China (8-9 September, 2026) and Salt Lake City, United States (Nov 9–12, 2026). Connect with our current graduated, incubating, and sandbox projects as the community gathers to further the education and advancement of cloud native computing. Learn more at kubecon.io
Kubeflow 2026 State of the Union: Orchestrating the Full ML Lifecycle - Yash Pal, student; Anya Kramar & Valentina Rodriguez Sosa, Red Hat
Kubeflow has grown into a comprehensive ecosystem that powers end-to-end machine learning on Kubernetes.
In this session, we provide a holistic ""State of the Union"" update across all major working groups. We will walk the audience through the lifecycle of a modern model, demonstrating how the unified SDK and Notebook Workspaces simplify authoring.
We will then trace how these workloads transition into scalable Kubeflow Pipelines, undergo distributed training and hyperparameter tuning via Trainer v2 and Katib, and are finally cataloged in the Model Registry before being deployed via KServe.
We will conclude by outlining the upcoming project roadmap and providing updates on how we can improve core Kubeflow components or build integration bridges to adjacent OSS AI projects.
