Enable Production ML with Databricks Feature Store

Discover how to overcome the challenges of productionalizing machine learning models with Databricks Feature Store in this 33-minute video. Learn about the first feature store built on the data lakehouse, which simplifies production ML by drawing data sources from a central lakehouse and making feature tables accessible for both machine learning and analytics use cases. Explore how Feature Store integrates seamlessly with Apache Spark™ and MLflow, enabling automatic lineage tracking and feature value lookup at inference time. Watch a demonstration of these capabilities in action and understand how to apply them to your ML projects. Gain insights into real-time features, online specifications, table publishing, and using Postman for testing. By the end of this video, you'll have a comprehensive understanding of how Databricks Feature Store can streamline your ML production process and help overcome common data-related obstacles.

Introduction
Overview
Data Challenges
Feature Store
Feature Store Demo
RealTime Features
Challenges
Code
Problem
Whats Next
Create Online Spec
Publish Table
Postman
Summary