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.
Pre-requisites
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.
Caveats
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.
Modules
See also: Feast quickstart, Feast x Great Expectations tutorial​
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
TBD
Versioning features / models in Feast
TBD
TBD
Data quality monitoring in Feast
TBD
TBD
Batch transformations
TBD
TBD
Stream transformations
TBD

Provider

Next - Getting started

Third party integrations

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Contents

Pre-requisites

Modules