This workshop aims to teach basic Feast concepts and walk you through focuses on how to achieve common 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)
| Description | Module |
|---|---|
| Feast Concepts and basic flows | Quickstart |
| Powering low latency online feature retrieval with Kafka, Spark, and Redis | Module 1 |
| Using remote registry and file sources, platform vs client user flows, on demand transformations | Module 2 |
| Fetching features for batch scoring | TBD |
| Feast Web UI | TBD |
| Versioning features / models in Feast | TBD |
| Data quality monitoring in Feast | TBD |
| Deploying a feature server to AWS Lambda | TBD |