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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

Modules

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
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
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