Learning by example

This workshop aims to teach users about Feast.

We explain concepts & best practices by example, and also showcase how to address common use cases.

This workshop assumes you have the following installed:

A local development environment that supports running Jupyter notebooks (e.g. VSCode with Jupyter plugin)
Python 3.7+
Java 11 (for Spark, e.g. brew install java11)
pip
Docker & Docker Compose (e.g. brew install docker docker-compose)
Terraform (docs)
AWS CLI
An AWS account setup with credentials via aws configure (e.g see AWS credentials quickstart)

Since we'll be learning how to leverage Feast in CI/CD, you'll also need to fork this workshop repository.

M1 Macbook development is untested with this flow. See also How to run / develop for Feast on M1 Macs.
Windows development has only been tested with WSL. You will need to follow this guide to have Docker play nicely.

These are meant mostly to be done in order, with examples building on previous concepts.

Time (min)

Description

Module

30-45

Setting up Feast projects & CI/CD + powering batch predictions

Module 0

15-20

Streaming ingestion & online feature retrieval with Kafka, Spark, Redis

Module 1

10-15

Real-time feature engineering with on demand transformations

Module 2

TBD

Feature server deployment (embed, as a service, AWS Lambda)

TBD

Versioning features / models in Feast

TBD

Data quality monitoring in Feast

TBD

Batch transformations

TBD

Stream transformations

TBD

Last updated 3 years ago