Speaker

Yash Pal

Yash Pal

Student, KubeFlow contributor

Mumbai, India

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Yash Pal is an open-source contributor and backend developer passionate about cloud-native and MLOps systems. He contributed to Kubeflow Notebooks 2.0, working on the Istio integration setup with guidance from maintainer Andy Stoneberg. He focuses on understanding how Kubernetes, Istio, and ML platforms interact at scale and enjoys sharing insights from his open-source journey.

Area of Expertise

  • Information & Communications Technology

Topics

  • AI
  • Observability
  • Kubernetes
  • kubecon
  • kubeflow
  • Observability and Monitoring
  • Data Observability

Kubeflow 2026 State of the Union: Orchestrating the Full ML Lifecycle

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.

Kubeflow 2026 State of the Union: Orchestrating the Full ML Lifecycle

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.

Yash Pal

Student, KubeFlow contributor

Mumbai, India

Actions

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