This workshop aims to teach users about Feast, an open-source feature store.
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+
- pip
- Docker & Docker Compose (e.g.
brew install docker docker-compose)
Since we'll be learning how to leverage Feast in CI/CD, you'll also need to fork this workshop repository.
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 |
| 10-15 | Streaming ingestion & online feature retrieval with Kafka, Spark, Redis | Module 1 |
| 10 | On demand feature views | 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 |