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  • Introduction
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  • Getting started
    • Quickstart
    • Concepts
      • Overview
      • Data source
      • Entity
      • Feature view
      • Feature service
      • Feature retrieval
    • Architecture
      • Overview
      • Feature repository
      • Registry
      • Offline store
      • Online store
      • Provider
    • FAQ
  • Tutorials
    • Overview
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    • Fraud detection on GCP
    • Real-time credit scoring on AWS
  • How-to Guides
    • Running Feast with 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
    • Upgrading from Feast 0.9
    • Adding a custom provider
    • Adding a new online store
    • Adding a new offline store
  • Reference
    • Data sources
      • File
      • BigQuery
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    • Offline stores
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      • Redshift
    • Online stores
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      • DynamoDB
    • Providers
      • Local
      • Google Cloud Platform
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    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feast CLI reference
    • Python API reference
    • Usage
  • Project
    • Contribution process
    • Development guide
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
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  • Deploying
  • Cleaning up

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  1. How-to Guides
  2. Running Feast with GCP/AWS

Deploy a feature store

PreviousCreate a feature repositoryNextBuild a training dataset

Last updated 3 years ago

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The Feast CLI can be used to deploy a feature store to your infrastructure, spinning up any necessary persistent resources like buckets or tables in data stores. The deployment target and effects depend on the provider that has been configured in your file, as well as the feature definitions found in your feature repository.

Here we'll be using the example repository we created in the previous guide, . You can re-create it by running feast init in a new directory.

Deploying

To have Feast deploy your infrastructure, run feast apply from your command line while inside a feature repository:

feast apply

# Processing example.py as example
# Done!

Depending on whether the feature repository is configured to use a local provider or one of the cloud providers like GCP or AWS, it may take from a couple of seconds to a minute to run to completion.

At this point, no data has been materialized to your online store. Feast apply simply registers the feature definitions with Feast and spins up any necessary infrastructure such as tables. To load data into the online store, run feast materialize. See for more details.

Cleaning up

If you need to clean up the infrastructure created by feast apply, use the teardown command.

Warning: teardown is an irreversible command and will remove all feature store infrastructure. Proceed with caution!

feast teardown

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feature_store.yaml
Create a feature store
Load data into the online store