LogoLogo
v0.22-branch
v0.22-branch
  • Introduction
  • Community
  • Roadmap
  • Changelog
  • Getting started
    • Quickstart
    • Concepts
      • Overview
      • Data source
      • Dataset
      • Entity
      • Feature view
      • Stream feature view
      • Feature retrieval
      • Point-in-time joins
      • Registry
    • Architecture
      • Overview
      • Feature repository
      • Registry
      • Offline store
      • Online store
      • Provider
    • Learning by example
    • Third party integrations
    • FAQ
  • Tutorials
    • Overview
    • Driver ranking
    • Fraud detection on GCP
    • Real-time credit scoring on AWS
    • Driver stats on Snowflake
    • Validating historical features with Great Expectations
    • Using Scalable Registry
    • Building streaming features
  • How-to Guides
    • Running Feast with Snowflake/GCP/AWS
      • Install Feast
      • Create a feature repository
      • Deploy a feature store
      • Build a training dataset
      • Load data into the online store
      • Read features from the online store
    • Running Feast in production
    • Deploying a Java feature server on Kubernetes
    • Upgrading from Feast 0.9
    • Adding a custom provider
    • Adding a new online store
    • Adding a new offline store
    • Adding or reusing tests
  • Reference
    • Codebase Structure
    • Data sources
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • Push
      • Kafka
      • Kinesis
      • Spark (contrib)
      • PostgreSQL (contrib)
    • Offline stores
      • File
      • Snowflake
      • BigQuery
      • Redshift
      • Spark (contrib)
      • PostgreSQL (contrib)
    • Online stores
      • SQLite
      • Redis
      • Datastore
      • DynamoDB
      • PostgreSQL (contrib)
    • Providers
      • Local
      • Google Cloud Platform
      • Amazon Web Services
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feature servers
      • Python feature server
      • Go-based feature retrieval
    • [Alpha] Web UI
    • [Alpha] Data quality monitoring
    • [Alpha] On demand feature view
    • [Alpha] AWS Lambda feature server
    • Feast CLI reference
    • Python API reference
    • Usage
  • Project
    • Contribution process
    • Development guide
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
Powered by GitBook
On this page
  • Pre-requisites
  • Modules

Was this helpful?

Edit on GitHub
Export as PDF
  1. Getting started

Learning by example

PreviousProviderNextThird party integrations

Last updated 2 years ago

Was this helpful?

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 ()

  • 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.

Caveats

  • 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.

Modules

These are meant mostly to be done in order, with examples building on previous concepts.

See https://github.com/feast-dev/feast-workshop

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

See also: ,

docs
AWS credentials quickstart
How to run / develop for Feast on M1 Macs
guide
Feast quickstart
Feast x Great Expectations tutorial