Learning by example
Last updated
Last updated
This workshop aims to teach users about Feast.
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+
Java 11 (for Spark, e.g. brew install java11
)
pip
Docker & Docker Compose (e.g. brew install docker docker-compose
)
Terraform ()
AWS CLI
An AWS account setup with credentials via aws configure
(e.g see )
Since we'll be learning how to leverage Feast in CI/CD, you'll also need to fork this workshop repository.
M1 Macbook development is untested with this flow. See also .
Windows development has only been tested with WSL. You will need to follow this to have Docker play nicely.
These are meant mostly to be done in order, with examples building on previous concepts.
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
See also: ,
See