This workshop aims to teach basic Feast concepts & best practices by example. We walk through how to address common use cases and architectures.
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+
- pip
- Docker & Docker Compose (e.g.
brew install docker docker-compose)
See also: Feast quickstart
These are meant mostly to be done in order, with examples building on previous concepts.
| Description | Module |
|---|---|
| Setting up Feast projects & CI/CD + powering batch predictions | Module 0 |
| Online feature retrieval with Kafka, Spark, Redis | Module 1 |
| On demand feature views | Module 2 |
| Versioning features / models in Feast | TBD |
| Data quality monitoring in Feast | TBD |
| Feature server deployment (embed, as a service, AWS Lambda) | TBD |