Creating a Feature Service to Group the Features Together

Your feature repository now has five features. You created the user_credit_card_issuer, user_transaction_counts, transaction_amount_is_high, and transaction_distance_from_home features. The feature user_home_location, was already defined. In this topic, you will create a Feature Service that groups these features together.

In your local feature repository, open the file feature_services/fraud_detection.py. In the file, uncomment the following code, which is a definition of the Feature Service.

from tecton import FeatureService

from features.batch_features.user_credit_card_issuer import user_credit_card_issuer
from features.batch_features.user_transaction_counts import user_transaction_counts

from features.batch_features.user_home_location import user_home_location

from features.on_demand_features.transaction_amount_is_high import (
    transaction_amount_is_high,
)
from features.on_demand_features.transaction_distance_from_home import (
    transaction_distance_from_home,
)

fraud_detection_feature_service = FeatureService(
    name="fraud_detection_feature_service",
    online_serving_enabled=True,
    features=[
        user_credit_card_issuer,
        transaction_amount_is_high,
        user_transaction_counts,
        user_home_location,
        transaction_distance_from_home,
    ],
)

The features list, shown above, contains the features to include in the feature service. online_serving_enabled is set to True to allow features to be retrieved from the online store.

In your terminal, run tecton apply to apply this Feature Service to your workspace.

note

Now that you have created the Feature Service, you are ready to proceed to the next part of this tutorial. In the next part, you will call the Feature Service to read feature data for training and inference, at which time you will see the output.