Feast is an open source feature store for machine learning. Feast is the fastest path to productionizing analytic data for model training and online inference.
Please see our documentation for more information about the project.
The above architecture is the minimal Feast deployment. Want to run the full Feast on Kubernetes? Click here.
pip install feast
feast init my_feature_repo
cd my_feature_repo
feast apply
from feast import FeatureStore
import pandas as pd
from datetime import datetime
entity_df = pd.DataFrame.from_dict({
"driver_id": [1001, 1002, 1003, 1004],
"event_timestamp": [
datetime(2021, 4, 12, 10, 59, 42),
datetime(2021, 4, 12, 8, 12, 10),
datetime(2021, 4, 12, 16, 40, 26),
datetime(2021, 4, 12, 15, 1 , 12)
]
})
store = FeatureStore(repo_path=".")
training_df = store.get_historical_features(
entity_df=entity_df,
feature_refs = [
'driver_hourly_stats:conv_rate',
'driver_hourly_stats:acc_rate',
'driver_hourly_stats:avg_daily_trips'
],
).to_df()
print(training_df.head())
# Train model
# model = ml.fit(training_df) event_timestamp driver_id driver_hourly_stats__conv_rate driver_hourly_stats__acc_rate
2021-04-12 08:12:10 1002 0.497279 0.357702
2021-04-12 10:59:42 1001 0.979747 0.008166
2021-04-12 15:01:12 1004 0.151432 0.551748
2021-04-12 16:40:26 1003 0.951506 0.753572
CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize-incremental $CURRENT_TIME
Materializing feature view driver_hourly_stats from 2021-04-14 to 2021-04-15 done!
from pprint import pprint
from feast import FeatureStore
store = FeatureStore(repo_path=".")
feature_vector = store.get_online_features(
feature_refs=[
'driver_hourly_stats:conv_rate',
'driver_hourly_stats:acc_rate',
'driver_hourly_stats:avg_daily_trips'
],
entity_rows=[{"driver_id": 1001}]
).to_dict()
pprint(feature_vector)
# Make prediction
# model.predict(feature_vector){
"driver_id": [1001],
"driver_hourly_stats__conv_rate": [0.49274],
"driver_hourly_stats__acc_rate": [0.92743],
"driver_hourly_stats__avg_daily_trips": [72]
}Please refer to the official documentation at Documentation
Feast is a community project and is still under active development. Please have a look at our contributing and development guides if you want to contribute to the project:
- Contribution Process for Feast
- Development Guide for Feast
- Development Guide for the Main Feast Repository
Thanks goes to these incredible people: