Open strickvl opened 8 months ago
Hey @strickvl , I want to contribute to this issue. I would like to request you to assign this issue to me.
Thank you! I will start working on it.
Thanks @Pistonamey! Let us know if you have any questions!
@strickvl Updates on the implementation: Made changes to the constants and enums file to recognize feature_store as a stack component and include feast as an available flavor.
Currently working on developing the Terraform module.
MLStacks framework notionally supports feature stores but lacks an explicit deployment option for Feast, a popular feature store for machine learning. This task involves integrating Feast as a
ComponentFlavorEnum
within MLStacks and implementing its deployment via Terraform.Task Description
To enhance MLStacks' feature store capabilities, this task aims to add Feast as a deployable feature store option. This requires updating enums and constants to recognize Feast as a component flavor and creating a Terraform module for deploying Feast on Kubernetes clusters, guided by the Feast repository's Terraform configuration.
Expected Outcome
ComponentFlavorEnum
insrc/mlstacks/enums.py
includes Feast as an option for feature store components.src/mlstacks/constants.py
is updated to supportfeature_store
as a stack component type, withfeast
as a permitted flavor.Steps to Implement
src/mlstacks/enums.py
to addfeast
to theComponentFlavorEnum
for feature stores.src/mlstacks/constants.py
to recognizefeature_store
as a stack component and includefeast
as an available flavor.Additional Context
By incorporating Feast as a feature store deployment option, MLStacks will significantly enhance its data management capabilities, offering users a robust and scalable solution for managing features in machine learning workflows.
Code of Conduct