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Quickstart

In this tutorial we will

  1. Deploy a local feature store with a Parquet file offline store and Sqlite online store.
  2. Build a training dataset using our time series features from our Parquet files.
  3. Materialize feature values from the offline store into the online store.
  4. Read the latest features from the online store for inference.

You can run this tutorial in Google Colab or run it on your local host, following the guided steps below.

Run in Google Colab

Step 1: Install Feast

Install the Feast SDK and CLI using pip:

pip install feast

Step 2: 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.

Step 3: 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

Step 4: 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,
    features=[
        "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

Step 5: 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

Step 6: Fetching feature vectors for inference

from pprint import pprint
from feast import FeatureStore

store = FeatureStore(repo_path=".")

feature_vector = store.get_online_features(
    features=[
        "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],
    'conv_rate': [0.49274],
    'acc_rate': [0.92743],
    '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!
  • For more tutorials, check the the Tutorials section.