This tutorial uses Feast with Scikit Learn to

1. Train a model locally using data from BigQuery
2. Test the model for online inference using Sqlite (for fast iteration)
3. Test the model for online inference using Firestore (to represent production)

1. Install Feast

pip install feast scikit-learn

(tested with Feast==0.10.2)

2. Set up a local feature store

cd driver_ranking/
feast apply
cd ..

3. Train a model

python train.py

4. Load data into your local sqlite online store

cd driver_ranking/
feast materialize-incremental 2022-01-01T00:00:00
cd ..

5. Test your model with your local sqlite online store

python predict.py

6. Set up your production feature store with GCP (uses Google Firestore)

Ensure that Google cloud has been configured

gcloud config set project SET_YOUR_GCP_PROJECT_HERE
gcloud auth application-default login

Change the provider field in driver_ranking/feature_store.yaml from local to gcp

Then apply and materialize data to Firestore

cd driver_ranking/
feast apply
feast materialize-incremental 2022-01-01T00:00:00
cd ..

7. Test your model with your remote Firestore online store

python predict.py

For production use its preferred to use a Google Cloud Storage based registry instead of a local repository. This allows multiple production systems to share the same source of truth for feature definitions.

Change feature_store.yaml to

project: driver_ranking
registry: gs://my-feature-store-bucket/registry.db
provider: gcp

Change predict.py and train.py to

self.fs = feast.FeatureStore(
    config=RepoConfig(
        project="driver_ranking",
        provider="gcp",
        registry="gs://my-feature-store-bucket/registry.db",
    )
)