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  • Introduction
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  • Changelog
  • Getting started
    • Quickstart
    • Concepts
      • Overview
      • Data source
      • Dataset
      • Entity
      • Feature view
      • Stream feature view
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      • Registry
    • Architecture
      • Overview
      • Feature repository
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      • Offline store
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    • 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
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    • Offline stores
      • File
      • Snowflake
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      • Spark (contrib)
      • PostgreSQL (contrib)
    • Online stores
      • SQLite
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      • PostgreSQL (contrib)
    • Providers
      • Local
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    • 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+
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  1. Tutorials

Driver ranking

Making a prediction using a linear regression model is a common use case in ML. This model predicts if a driver will complete a trip based on features ingested into Feast.

PreviousOverviewNextFraud detection on GCP

Last updated 2 years ago

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In this example, you'll learn how to use some of the key functionality in Feast. The tutorial runs in both local mode and on the Google Cloud Platform (GCP). For GCP, you must have access to a GCP project already, including read and write permissions to BigQuery.

This tutorial guides you on how to use Feast with . You will learn how to:

  • Train a model locally (on your laptop) using data from

  • Test the model for online inference using (for fast iteration)

  • Test the model for online inference using (for production use)

Try it and let us know what you think!

Driver Ranking Example
Scikit-learn
BigQuery
SQLite
Firestore
Run in Google Colab
View Source in Github