Feature Store in Machine Learning

Feature store in machine learning is the concept to store features in both online and offline stores for model training and serving purposes. Feature store make sure to provide the consistency between the data used for model training and the data used during online serving to models. In other words, it guarantees that you’re serving the same data to models during training and prediction, eliminating training-prediction skew. Feast is one of the open source tools used for feature store. Feature serving is one of the important practice used in MLOps

You can find the theoretical concepts in the feast website itself. Link Here I want to explain the practical implementation aspect of feast. For the same I have created a video explaining each of the below steps used in feast online and offline feature serving to models.

Prepare data set and store in parquet format
Create an event_timestamp column to the data
Create a unique ID and add as a column to the data
Do feast init and create feature repo structure (to be modified later)
create feature_definition.py file inside feature repo and define data source and feature views
do feast apply to register and deploy features to offline store
load historical features using get_historical_features from offline feature store
Train the model using offline features
use feast materialize-incremental to load features to online store
get online features using get_online_features
do the prediction using the data from online feature store.

Please refer the below video for end to end code explanation.