Overview

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.

Architecture

The above architecture is the minimal Feast deployment. Want to run the full Feast on Kubernetes? Click here.

Getting Started

1. Install Feast

pip install feast

2. Create a feature repository

feast init my_feature_repo
cd my_feature_repo

3. Register your feature definitions and set up your feature store

feast apply

4. Build a training dataset

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

5. Load feature values into your online store

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!

6. Read online features at low latency

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]
}

Important resources

Please refer to the official documentation at Documentation

• Quickstart
• Roadmap
• Feast on Kubernetes
• Change Log
• Slack (#Feast)

Contributing

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

Contributors ✨

Thanks goes to these incredible people: