Quickstart

In this tutorial we will
Deploy a local feature store with a Parquet file offline store and Sqlite online store.
Build a training dataset using our time series features from our Parquet files.
Materialize feature values from the offline store into the online store.
Read the latest features from the online store for inference.
Install Feast
Install the Feast SDK and CLI using pip:
pip install feast
Create a feature repository
Bootstrap a new feature repository using feast init from the command line:
feast init feature_repo
cd feature_repo
Creating a new Feast repository in /home/Jovyan/feature_repo.
Register feature definitions and deploy your feature store
The apply command registers all the objects in your feature repository and deploys a feature store:
feast apply
Registered entity driver_id
Registered feature view driver_hourly_stats
Deploying infrastructure for driver_hourly_stats
Generating training data
The apply command builds a training dataset based on the time-series features defined in the feature repository:
from datetime import datetime
​
import pandas as pd
​
from feast import FeatureStore
​
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())
event_timestamp   driver_id  driver_hourly_stats__conv_rate  driver_hourly_stats__acc_rate  driver_hourly_stats__avg_daily_trips
2021-04-12        1002       0.328245                        0.993218                       329
2021-04-12        1001       0.448272                        0.873785                       767
2021-04-12        1004       0.822571                        0.571790                       673
2021-04-12        1003       0.556326                        0.605357                       335
Load features into your online store
The materialize command loads the latest feature values from your feature views into your online store:
CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize-incremental $CURRENT_TIME
Fetching feature vectors for inference
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)
{
    'driver_id': [1001],
    'driver_hourly_stats__conv_rate': [0.49274],
    'driver_hourly_stats__acc_rate': [0.92743],
    'driver_hourly_stats__avg_daily_trips': [72],
}
Next steps
Follow our Getting Started guide for a hands tutorial in using Feast
Join other Feast users and contributors in Slack and become part of the community!
Introduction
Getting started
Last updated 4 weeks ago