Kubeflow 1.11 release blog by tarekabouzeid · Pull Request #190 · kubeflow/blog

tarekabouzeid · 2026-02-08T19:51:33Z

Initial draft for 1.11 release.

/cc @kubeflow/wg-data-leads @kubeflow/kubeflow-sdk-team @kubeflow/kubeflow-trainer-team @kubeflow/wg-training-leads @kubeflow/wg-pipeline-leads @kubeflow/wg-notebooks-leads @kubeflow/wg-manifests-leads @kubeflow/kubeflow-hub-team @andreyvelich @juliusvonkohout

Signed-off-by: Tarek Abouzeid <tarek.abouzeid91@gmail.com>

google-oss-prow · 2026-02-08T19:51:40Z

@tarekabouzeid: GitHub didn't allow me to request PR reviews from the following users: kubeflow/wg-pipeline-leads, kubeflow/wg-notebooks-leads, kubeflow/kubeflow-sdk-team, kubeflow/kubeflow-trainer-team, kubeflow/wg-training-leads, kubeflow/wg-data-leads, kubeflow/wg-manifests-leads, kubeflow/kubeflow-hub-team.

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In response to this:

Initial draft for 1.11 release.

/cc @kubeflow/wg-data-leads @kubeflow/kubeflow-sdk-team @kubeflow/kubeflow-trainer-team @kubeflow/wg-training-leads @kubeflow/wg-pipeline-leads @kubeflow/wg-notebooks-leads @kubeflow/wg-manifests-leads @kubeflow/kubeflow-hub-team @andreyvelich @juliusvonkohout

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tarilabs

thank you @tarekabouzeid

Co-authored-by: Matteo Mortari <matteo.mortari@gmail.com> Signed-off-by: Tarek Abouzeid <tarek.abouzeid91@gmail.com>

Signed-off-by: Tarek Abouzeid <tarek.abouzeid91@gmail.com>

Co-authored-by: Andrey Velichkevich <andrey.velichkevich@gmail.com> Signed-off-by: Tarek Abouzeid <tarek.abouzeid91@gmail.com>

josh-indemnifyai · 2026-02-09T18:29:57Z

+Kubeflow AI Reference Platform 1.11 delivers substantial platform improvements focused on scalability, security, and operational efficiency. The release reduces per namespace overhead, strengthens multi-tenant defaults, and improves overall reliability for running Kubeflow at scale on Kubernetes.
+
+## Highlight features
+


The Model Registry / KServe CSI and reconciliation loop integration seems like time-saving automation that delivers more accurate and simplified model deployment and management. @tarilabs ? is this true...with the Model Catalog, users can point, click, deploy and auto-manage...Maybe we have promoted before, but this combo brings value of two KF apps together to produce a greater good.

Signed-off-by: Tarek Abouzeid <tarek.abouzeid91@gmail.com>

tarekabouzeid · 2026-02-16T19:35:50Z

@thesuperzapper @andyatmiami Added central dashboard and notebooks section.
c.c @andreyvelich @juliusvonkohout

andreyvelich · 2026-02-17T16:18:56Z

+
+Katib hyperparameter tuning remains compatible with Trainer v2, allowing users to optimize model hyperparameters alongside the new training workflow.
+
+A major addition is the integration with Kubeflow SDK ([KEP-46](https://github.com/kubeflow/sdk/tree/main/docs/proposals/46-hyperparameter-optimization), [PR #124](https://github.com/kubeflow/sdk/pull/124)). The new `OptimizerClient` allows users to define and run hyperparameter experiments directly from Python notebooks without writing YAML. You can configure search spaces, objectives, and algorithms using `OptimizerClient().optimize()`. Each trial runs as a TrainJob with different hyperparameter values, and training code can report metrics using simple Python functions. The client includes standard methods for managing jobs: `create_job()`, `get_job()`, `list_jobs()`, and `delete_job()`.


@tarekabouzeid Can we have dedicated section for Kubeflow SDK since it will be the first AI reference platform release when users can do pip install kubeflow to start working with AI workloads.
cc @kubeflow/kubeflow-sdk-team

@andreyvelich Added that as well.

Signed-off-by: Tarek Abouzeid <tarek.abouzeid91@gmail.com>

andreyvelich

/lgtm
/approve
Thanks @tarekabouzeid!

andreyvelich · 2026-02-23T17:08:25Z

/hold for ci

Signed-off-by: Tarek Abouzeid <tarek.abouzeid91@gmail.com>

jaiakash

/lgtm
We are good for this right, only issue is with netlify build.

thesuperzapper · 2026-02-26T03:27:34Z

PR to fix the Netelify builds (which are only for preview btw, as website is hosted on GitHub pages): #191

jaiakash · 2026-02-26T17:38:07Z

/retest

tarekabouzeid · 2026-02-26T17:53:33Z

@andreyvelich can you please lgtm ? And thanks so much for your help @thesuperzapper !

andreyvelich · 2026-02-26T17:57:41Z

Thanks @tarekabouzeid!
/lgtm
/approve

google-oss-prow · 2026-02-26T17:57:58Z

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thesuperzapper · 2026-02-27T02:25:26Z

/unhold

Kubeflow 1.11 first draft

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		Kubeflow AI Reference Platform 1.11 delivers substantial platform improvements focused on scalability, security, and operational efficiency. The release reduces per namespace overhead, strengthens multi-tenant defaults, and improves overall reliability for running Kubeflow at scale on Kubernetes.

		## Highlight features


		Katib hyperparameter tuning remains compatible with Trainer v2, allowing users to optimize model hyperparameters alongside the new training workflow.

		A major addition is the integration with Kubeflow SDK ([KEP-46](https://github.com/kubeflow/sdk/tree/main/docs/proposals/46-hyperparameter-optimization), [PR #124](https://github.com/kubeflow/sdk/pull/124)). The new `OptimizerClient` allows users to define and run hyperparameter experiments directly from Python notebooks without writing YAML. You can configure search spaces, objectives, and algorithms using `OptimizerClient().optimize()`. Each trial runs as a TrainJob with different hyperparameter values, and training code can report metrics using simple Python functions. The client includes standard methods for managing jobs: `create_job()`, `get_job()`, `list_jobs()`, and `delete_job()`.

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