Feast Quickstart (with IBM Cloud)

If you haven't already, check out the quickstart guide on Feast's website (http://docs.feast.dev/quickstart), which uses this repo. A quick view of what's in this repository's feature_repo/ directory:

• data/ contains raw demo parquet data
• feature_repo/example_repo.py contains demo feature definitions
• feature_repo/feature_store.yaml contains a demo setup configuring where data sources are IBM Cloud redis service for online store and IBM Cloud Data Engine for offline store.
• feature_repo/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.

To run a demo with IBM Cloud services:

1. Procure IBM Cloud Redis and IBM Cloud Data Engine services

2. Set below environment variables

   
   export DATA_ENGINE_API_KEY=<DATA_ENGINE_API_KEY>
   export IBM_CLOUD_OBJECT_STORE_URL=<IBM_CLOUD_OBJECT_STORE_URL>
   export REDIS_HOST=<REDIS_HOST>
   export REDIS_PORT=<REDIS_PORT>
   export REDIS_PASSWORD=<REDIS_PASSWORD>
   export REDIS_CERT_PATH=<REDIS_CERT_PATH>
   

3. Download ibm-cloud-data-engine plugin project and configure path in pyproject.toml

4. Install dependencies

   poetry install
   

5. Run feast apply to create/update feature store deployment.

   poetry run feast -c ./feature_repo apply
   

6. Run training by retriving historical feature information from feature store

   poetry run python training.py
   

7. Materialize features from offline to online store

   poetry run feast -c ./ materialize '<START_TIMESTAMP>'  '<END_TIMESTAMP>'
   

8. Run inference during production to retrieve features from online store.

   poetry run python inference.py