This technical video course provides a comprehensive guide to getting started with the FEAST feature store, focusing on operational machine learning challenges. By the end of the course, learners will understand how to access raw data, build features, combine them for training data, serve features in production, and monitor them effectively. The course covers the installation and initialization of FEAST, transforming feature values, working with online and offline stores, and utilizing historical data for scoring models. The intended audience for this course includes AI engineers, data scientists, and machine learning practitioners looking to enhance their skills in operationalizing analytic data for model training and online inference.
An AI Engineer Technical Guide to Feature Store with FEAST
Overview
Syllabus
Video Start
Feature Store content intro
Feature Store - What is it, and how it helps?
Feature store - Details
Google Feature Store - Vertex
DataBricks Feature Store
Tecton Feature Store - FEAST
Feature Store Definition
Jupyter Notebook: Feast Installation/Init
Understanding Source Data
Setting Feature Store - Creating registry catalog and online store
Feast Architecture Review after hands-on example
Online store sqlite review
Transforming the feature values from source data
Understanding Online and offline store
Features added to online store validation
Machine Learning with online features
Saving Model
Using historical data and saved model to score
Content Review
GitHub review to Jupyter Notebook
Plans to use Postgresql in place of sqllite as online store
Credits
Taught by
Prodramp
