I am currently working on a research project about Feature Stores with Feast and I am looking for examples of experiments or case studies that demonstrate the value of using Feature Stores in data science projects.
Currently I’m preparing some experiments with Feast. Specially I’m focus on:
· Trying to prove that using Feast improve speed of getting features on data either stored locally or on GCP with many rows, tables and many data sources.
· Secondly, I hope Feast also will help with feature engineering during calculating features on demand and will takes less time in getting prediction from model.
· Hope also that Feast improve ML quality by reducing training and retraining time and also improve models score by data validation
· Finally, I’ll prove that Feast will reduce costs in GCP cloud.
If any of you have experience or knowledge in this area, I would greatly appreciate any proposals for new expermients.
