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