Migrate to Managed Kubeflow
This section provides an overview of how to migrate your machine learning projects to Managed Kubeflow, the recommended standard on CAIP. Managed Kubeflow offers a fully supported, secure, and scalable platform for both experimentation and production use cases. All new and existing use cases must migrate to ensure continued support and access to the latest features.
You will find dedicated migration guides for:
If you want to learn more about Kubeflow in general, you can read the Kubeflow overview or compare the different Kubeflow flavours.
General Migration Steps
- Complete onboarding prerequisites to ensure you have the necessary access and resources.
- Understand the staging concept for organizing pipelines and models within Managed Kubeflow.
- Set up your Managed Kubeflow workspace as described in the onboarding guide, and link your GitHub repositories and CDH use cases/providers.
- Migrate your code, pipelines, models, data connections, CI/CD automation, and additional infrastructure (e.g., secrets, configmaps, custom resources) to your Managed Kubeflow namespace. Test your end-to-end workflows to ensure all components run as expected.
Need Support?
If you need help with any migration tasks or encounter issues, you can always reach out to our support team via ITSM Next. Simply open an incident ticket for the "Connected AI Platform" service offering. For detailed, step-by-step instructions on how to raise a support request, please see our Support page. Our team is available to assist you at any stage of your migration to Managed Kubeflow.