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 | |
15-20 | Streaming ingestion & online feature retrieval with Kafka, Spark, Redis | |
10-15 | Real-time feature engineering with on demand transformations | |
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 |
Last updated