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  • Introduction
  • Quickstart
  • Getting started
    • 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
  • Community
  • Roadmap
  • Changelog
  • Concepts
    • Overview
    • Feature view
    • Data model
    • Online store
    • Offline store
    • Provider
    • Architecture
  • Reference
    • Data sources
      • BigQuery
      • File
    • Offline stores
      • File
      • BigQuery
    • Online stores
      • SQLite
      • Redis
      • Datastore
    • Providers
      • Local
      • Google Cloud Platform
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feast CLI reference
    • Python API reference
    • Usage
  • Feast on Kubernetes
    • Getting started
      • Install Feast
        • Docker Compose
        • Kubernetes (with Helm)
        • Amazon EKS (with Terraform)
        • Azure AKS (with Helm)
        • Azure AKS (with Terraform)
        • Google Cloud GKE (with Terraform)
        • IBM Cloud Kubernetes Service (IKS) and Red Hat OpenShift (with Kustomize)
      • Connect to Feast
        • Python SDK
        • Feast CLI
      • Learn Feast
    • Concepts
      • Overview
      • Architecture
      • Entities
      • Sources
      • Feature Tables
      • Stores
    • Tutorials
      • Minimal Ride Hailing Example
    • User guide
      • Overview
      • Getting online features
      • Getting training features
      • Define and ingest features
      • Extending Feast
    • Reference
      • Configuration Reference
      • Feast and Spark
      • Metrics Reference
      • Limitations
      • API Reference
        • Go SDK
        • Java SDK
        • Core gRPC API
        • Python SDK
        • Serving gRPC API
        • gRPC Types
    • Advanced
      • Troubleshooting
      • Metrics
      • Audit Logging
      • Security
      • Upgrading Feast
  • Contributing
    • Contribution process
    • Development guide
    • Versioning policy
    • Release process
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  • Using Feast
  • Defining feature tables and ingesting data into Feast
  • Retrieving historical features for training
  • Retrieving online features for online serving

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  1. Feast on Kubernetes
  2. User guide

Overview

PreviousUser guideNextGetting online features

Last updated 3 years ago

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Using Feast

Feast development happens through three key workflows:

Defining feature tables and ingesting data into Feast

Feature creators model the data within their organization into Feast through the definition of that contain . Feature tables are both a schema and a means of identifying data sources for features, and allow Feast to know how to interpret your data, and where to find it.

After registering a feature table with Feast, users can trigger an ingestion from their data source into Feast. This loads feature values from an upstream data source into Feast stores through ingestion jobs.

Visit to learn more about them.

Retrieving historical features for training

In order to generate a training dataset it is necessary to provide both an and feature references through the to retrieve historical features. For historical serving, Feast requires that you provide the entities and timestamps for the corresponding feature data. Feast produces a point-in-time correct dataset using the requested features. These features can be requested from an unlimited number of feature sets.

Retrieving online features for online serving

Online retrieval uses feature references through the to retrieve online features. Online serving allows for very low latency requests to feature data at very high throughput.

Feast Online Serving API
Getting online features
Define and load feature data into Feast
Retrieve historical features for training models
Retrieve online features for serving models
feature tables
data sources
Define and ingest features
entity dataframe
Feast SDK
Getting training features
feature tables