A quick view of what's in this repository:

• data/ contains raw demo parquet data
• example_repo.py contains demo feature definitions
• feature_store.yaml contains a demo setup configuring where data sources are
• test_workflow.py showcases how to run all key Feast commands, including defining, retrieving, and pushing features.

You can run the overall workflow with python test_workflow.py.

1. feature_store.yaml points to a local file as a registry. You'll want to setup a remote file (e.g. in S3/GCS) or a SQL registry. See registry docs for more details.
2. This example uses an already setup BigQuery Feast data warehouse as the offline store to generate training data. You'll need to connect your own BigQuery instance to make this work.
3. Setup CI/CD + dev vs staging vs prod environments to automatically update the registry as you change Feast feature definitions. See docs.
4. (optional) Regularly scheduled materialization to power low latency feature retrieval (e.g. via Airflow). See Batch data ingestion for more details.
5. (optional) Deploy feature server instances with feast serve to expose endpoints to retrieve online features.
   • See Python feature server for details.
   • Use cases can also directly call the Feast client to fetch features as per Feature retrieval