~/panel

Engaging the Kubeflow Community: Building an Enterprise-Ready AI/ML Platform

on YouTube 44m
Panelat Cloud Native + Kubernetes AI Day Europe 2025 · on Tuesday, 2025-04-01· at 11:50· Level 1 | Hall Entrance N10 | Room G· [machine-learning]

speakers

name:Yuan Tang
website:https://terrytangyuan.github.io/about/
name:Andrey Velichkevich
github:andreyvelich
name:Andreea Munteanu
links:none on file
name:Johnu George
links:none on file
name:Ronen Dar
links:none on file

resources

video:https://youtu.be/eZYBILVeaWY
slides:none on file

summary

Kubeflow now stitches operators and tools like Knative, Istio, KServe, the Trainer operator, KFP pipelines, model-registry and Spark operator into a composable MLOps stack, claims enterprise deployments on any Kubernetes, and starts supporting GenAI with one-click finetuning plus LM blueprints and Arrow-based caching for large data. Users report gaps: GPU scheduling and cold-start latency lead to open-source efforts like the Kai scheduler and a model-streaming utility; documentation and inference tooling remain the biggest adoption blockers while incubation toward CNCF graduation continues.

AI-written summary · about

chapters (24)

  1. 0:00CubeFlow community panel intro
  2. 0:43Panelists' backgrounds and roles
  3. 3:11CubeFlow enterprise features
  4. 5:41CubeFlow components evolution
  5. 7:25User experiences and integrations
  6. 10:59Vendor perspectives on CubeFlow
  7. 13:07CubeFlow governance and growth
  8. 15:13Enterprise features and Canonical's role
  9. 16:38Kubernetes and AI/ML ecosystems
  10. 17:43CubeFlow for generative AI and LLMs
  11. 20:33CubeFlow upcoming features
  12. 21:57NVIDIA's Kai scheduler contributions
  13. 23:03AI inference challenges and opportunities
  14. 24:32Red Hat's CubeFlow vision
  15. 25:57CubeFlow CNCF incubation
  16. 27:46LM Blueprints for Kubernetes ML
  17. 29:12CubeFlow enterprise adoption
  18. 32:25Extending CubeFlow Pipelines
  19. 34:14CubeFlow scope and composability
  20. 36:03AI playgrounds and onboarding
  21. 37:30CubeFlow components and contributors
  22. 38:54CubeFlow documentation gaps
  23. 40:15CubeFlow adoption challenges
  24. 42:42Contributing to CubeFlow and closing

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