Real-time Credit Scoring with Feast on Local Setup
This tutorial is built from the original feast-aws-credit-scoring-tutorial.
This tutorial demonstrates the use of Feast as part of a real-time credit scoring application.
- The primary training dataset is a loan table. This table contains historic loan data with accompanying features. The dataset also contains a target variable, namely whether a user has defaulted on their loan.
- Feast is used during training to enrich the loan table with zipcode and credit history features from the data folder.
- Feast is also used to serve the latest zipcode and credit history features for online credit scoring using Redis
To get a better feel of what this example entails, you can view the steps outlined below in notebook
form in demo_walkthrough.ipynb.
- Python 3.12.11
- Registry: file
- Offline Storage: dask
- Online Storage: Redis
You can setup the storages with Podman or Docker:
- Setup an online store with Redis by Podman:
podman pull redis:7.4.5
podman run -d -p 6379:6379 --name redis -e "ALLOW_EMPTY_PASSWORD=yes" docker.io/library/redis:7.4.5
Install Feast using uv
uv sync
We have already set up a feature repository in feature_repo/. As a result, all we have to do is configure the feature_store.yaml/ in the feature repository. Please set the connection string of the Postgresql and Redis according to your local infra setup.
Deploy the feature store by running apply from within the feature_repo/ folder
cd feature_repo/
feast apply
Next we load features into the online store using the materialize-incremental command. This command will load the
latest feature values from a data source into the online store.
CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize-incremental $CURRENT_TIME
Alternatively, you may have to run
CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize 1990-01-01T00:00:00 $CURRENT_TIME
Return to the root of the repository
cd ..
Finally, we train the model using a combination of loan data from the parque file under the ./data folder and our zipcode and credit history features from duckdb (with Filesource). And then we test online inference by reading those same features from Redis.
python run.py
The script should then output the result of a single loan application
loan rejected!
Serving Demo and OpenAPI docs
You can run
python app.py
And you'll be able to see the endpoints by going to http://127.0.0.1:8888/docs#/.
Current the Go Feature Server only supports "file", AWS "s3" and GCP "gs" storage. In this demo, we choose "file".
Steps:
- terminate the previous running app.py if it is still running.
- start the Feast feature transformation server:
python app_with_transformation_server.py
- start the Go feature server, assume you have built the Go binary and named it as 'feast_go':
./feast_go -chdir ./feature_repo -port 8080
- test the URI "http://localhost:8080/health". We suppose to see 'Healthy' word be displayed.
- test the following post for testing get-online-features. Make sure the feast materialize command have executed.
curl -X POST \
"http://localhost:8080/get-online-features" \
-d '{
"features": [
"zipcode_features:city",
"zipcode_features:state",
"zipcode_features:location_type",
"zipcode_features:tax_returns_filed",
"zipcode_features:population",
"zipcode_features:total_wages",
"credit_history:credit_card_due",
"credit_history:mortgage_due",
"credit_history:student_loan_due",
"credit_history:vehicle_loan_due",
"credit_history:hard_pulls",
"credit_history:missed_payments_2y",
"credit_history:missed_payments_1y",
"credit_history:missed_payments_6m",
"credit_history:bankruptcies",
"total_debt_calc:total_debt_due"
],
"entities": {
"dob_ssn": [
"19630621_4278"
],
"zipcode": [
76104
],
"loan_amnt": [
35000
]
}
}' | jq
Example returned feature values:
{
"metadata": {
"feature_names": [
"dob_ssn",
"zipcode",
"city",
"state",
"location_type",
"tax_returns_filed",
"population",
"total_wages",
"credit_card_due",
"mortgage_due",
"student_loan_due",
"vehicle_loan_due",
"hard_pulls",
"missed_payments_2y",
"missed_payments_1y",
"missed_payments_6m",
"bankruptcies",
"total_debt_due"
]
},
"results": [
{
"values": [
"19630621_4278"
]
},
{
"values": [
76104
]
},
{
"values": [
"FORT WORTH"
]
},
{
"values": [
"TX"
]
},
{
"values": [
"PRIMARY"
]
},
{
"values": [
6058
]
},
{
"values": [
10534
]
},
{
"values": [
142325465
]
},
{
"values": [
null
]
},
{
"values": [
null
]
},
{
"values": [
null
]
},
{
"values": [
null
]
},
{
"values": [
null
]
},
{
"values": [
null
]
},
{
"values": [
null
]
},
{
"values": [
null
]
},
{
"values": [
null
]
},
{
"values": [
null
]
}
]
}