Explore the open-source Hopsworks feature store for Python-centric enterprise data science in this 55-minute webinar from Open Data Science. Learn how to manage features for training and serving models, including native Python support for feature engineering, pipelines, low latency access, online transformation functions, and data validation. Discover a Python-based domain-specific language for joining and retrieving features from data warehouses and lakes without SQL. Follow an end-to-end use case demonstrating the process from raw data to operationalized prediction service. Gain insights into feature store concepts, ML operations, data ingestion, feature pipelines, real-time features, point-in-time correct joins, and incremental training. Witness a live demo of the Hopsworks platform, covering project setup, data preview, feature group activities, training datasets, and an intrusion detection example using log click data.
Hopsworks - The Python-Centric Enterprise Feature Store
Overview
Syllabus
Introduction
What is a feature store
How does a business add value
ML Operations
Feature Store for ML
How do I get data into it
How do we write feature pipelines
Writing data to the feature store
Feature views
Python
Feature Pipeline
Feature Quality
RealTime Features
Feature View
Point in Time Correct Join
Preengineered features
Incremental training
Saving training data
Hopsworks properties
Hopsworks demo
Demo project
Data preview
Feature group activity
Training data sets
Intrusion detection example
Sample log click logs
Models
Model Data
Connecting to the Feature Store
Creating an API Key
Accessing the Platform
Predicting
Taught by
Open Data Science
