[kubeflow] [feast] [PROPOSAL] Donating Feast to Kubeflow

[Francisco Arceo]

Dear Feast and Kubeflow communities-

The Feast maintainers are proposing donating Feast to Kubeflow. We believe there are several benefits to this proposal. 

In particular:

1. Incorporating Feast into Kubeflow will help formally fill a needed gap for Kubeflow in the AI/ML Lifecycle.
2. Feast will also allow the Data WG to have an answer for the online serving of features. This will also nicely complement the Spark Operator as Feast supports batch and stream processing using Spark as an offline store.
3. The Feast community is healthy and the users will further grow the Kubeflow community.
4. Feast is expanding its scope to support Generative AI and RAG as a first-class citizen (retrieval/vector search in particular), which will help ensure Kubeflow has a solution for RAG.
5. With the inclusion of Feast, we can provide end-to-end demos of development and production AI/ML and suggested patterns for stitching the Kubeflow products together so that MLOps engineers, ML Engineers, and AI engineers can be impactful immediately after deploying Kubeflow.
6. We, the Feast maintainers, are just as committed to Feast as we have ever been and we believe this will meaningfully enhance Kubeflow and result in Kubeflow getting the benefit of my contributions and the contributions of the Feast community.

For more details, please see the proposal document or community adoption PR.

If you have any questions, feedback, or thoughts, please feel free to share them on this Github Issue!

Have a great weekend,

Francisco

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Francisco Javier Arceo

franciscojavierarceo.github.io