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Feature Store Bootstrap

$39

Feature store patterns using Feast, feature engineering pipelines, and offline/online serving configurations.

📁 8 files🏷 v1.0.0
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📁 File Structure 8 files

feature-store-bootstrap/ ├── LICENSE ├── README.md ├── config.example.yaml ├── docs/ │ ├── checklists/ │ │ └── pre-deployment.md │ ├── overview.md │ └── patterns/ │ └── pattern-01-offline-online-sync.md └── templates/ └── config.yaml

📖 Documentation Preview README excerpt

Feature Store Bootstrap

Production-ready feature store setup using Feast with feature engineering pipelines and offline/online serving patterns. Go from ad-hoc feature computation to a centralized, reusable feature platform.

What's Included

  • Feast feature store configuration and deployment
  • Feature engineering pipeline templates
  • Offline feature serving for training
  • Online feature serving for real-time inference
  • Feature validation and monitoring
  • Point-in-time correct feature retrieval patterns
  • Entity and feature view definitions

Quick Start


# 1. Copy the example config
cp config.example.yaml config.yaml

# 2. Initialize the Feast feature repository
feast init my_feature_repo

# 3. Apply feature definitions
feast apply

# 4. Materialize features to online store
feast materialize-incremental $(date -u +"%Y-%m-%dT%H:%M:%S")

Prerequisites

  • Python 3.9+
  • Feast 0.30+
  • Redis (for online store) or DynamoDB/Bigtable
  • Data warehouse access (BigQuery, Redshift, or Snowflake)

Contents


feature-store-bootstrap/
  config.example.yaml
  docs/
    overview.md
    patterns/
      pattern-01-*.md
    checklists/
      pre-deployment.md
  templates/
    config.yaml

Support

For questions or issues, contact: megafolder122122@hotmail.com

License

MIT License - Copyright 2026 Jesse Mikkola. See LICENSE for details.

📄 Code Sample .yaml preview

config.example.yaml # Feature Store Bootstrap - Example Configuration # Copy this file to config.yaml and update values for your environment feast: project: "my_ml_project" registry: "./feature_repo/registry.db" provider: "local" # For production: "gcp", "aws", or "azure" offline_store: type: "file" # For BigQuery: type: "bigquery", project: "your-gcp-project" # For Redshift: type: "redshift", cluster_id: "your-cluster" online_store: type: "sqlite" path: "./feature_repo/online_store.db" # For Redis: type: "redis", connection_string: "redis://localhost:6379" # For DynamoDB: type: "dynamodb", region: "us-east-1" materialization: interval_minutes: 60 incremental: true feature_engineering: pipelines: - name: "user_features" source: "users_table" schedule: "0 * * * *" # Hourly - name: "transaction_features" source: "transactions_table" schedule: "*/15 * * * *" # Every 15 minutes logging: level: "INFO"
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