Talk: building a Feature Store with Dagster and Ray

While working as an MLOps Engineer at Sanas I designed and developed the Feature Store used for model training and other workloads. The Feature Store had around 10 deep learning and statistical features (with cross-features dependencies), every feature having 10-40M files.

Dagster was used for orchestration, and Ray (KubeRay) — for scaling the jobs. I’ve written a custom RayClusterResource which handled automatic KubeRay cluster provisioning for Dagster’s ops and assets. It was possible to write something like this:

def my_feature(ray_cluster_my_feature: MyFeatureRayClusterResource):

to automatically run the @asset body in an auto-scaling KubeRay cluster on Kubernetes.

This talk at the Dagster Community Meetup explains the solution and how I’ve arrived at it in more details.