Serve your features in production

Feast is an open-source feature store. It is the fastest path to operationalizing analytic data for model training and online inference.

Get started

Teams running or contributing to Feast

Feast in four steps

Step 1

Set up a feature store

$ pip install feast

$ feast init

$ feast apply

Step 2

Generate point-in-time correct training datasets from your offline features

store = FeatureStore()

order_df = pd.from_csv('orders_df');

training_data = store.get_historical_features(
    entity_df = orders_df,
    feature_refs = [
        "driver_stats:conv_rate",
        "driver_stats:avg_daily_trips",
        "customer_profile:current_balance",
        "customer_profile:avg_passenger_count",
        "customer_profile:lifetime_trip_count",
    ],
)

model.fit(training_data)

Step 3

Load your features into an online store

$ feast materialize-incremental

Step 4

Read your features at low latency

features = store.get_online_features(
    feature_refs = [
        "driver_stats:conv_rate",
        "driver_stats:avg_daily_trips",
        "customer_profile:current_balance",
        "customer_profile:avg_passenger_count",
        "customer_profile:lifetime_trip_count",
    ],
    entity_rows = [{
        "driver_id" : "MvIDnSG3",
        "customer_id" : "yQlM1SRs"
    }],
)

model.predict(features)

Operationalize your analytics data

Feast operationalizes your offline data so it’s available for real-time predictions, without building custom pipelines.

Ensure consistency across training and serving

Feast guarantees you’re serving the same data to models during training and inference, eliminating training-serving skew.

Reuse your current infrastructure

Feast doesn’t require the deployment and ongoing management of dedicated infrastructure.

It runs on top of cloud managed services; reusing your existing infrastructure and spinning up new resources when needed.

Standardize your data workflows across teams

Feast brings standardization and consistency to your data engineering workflows across models and teams. Many teams use Feast as the foundation of their internal ML platforms.

FAQ

Is Feast a feature computation system?

No. Even though some feature stores include transformations, Feast purely manages retrieval. Feast is used alongside a separate system that computes feature values. Most often, these are pipelines written in SQL or a Python Dataframe library and scheduled to run periodically.

If you need a managed feature store that provides feature computation, check out Tecton.

How do I install and run Feast?

Feast is a Python library + optional CLI. You can install it using pip.

You might want to periodically run certain Feast commands (e.g. `feast materialize-incremental`, which updates the online store.) We recommend using schedulers such as Airflow or Cloud Composer for this.

For more details, please see the quickstart guide

What data stores does Feast support?

Feast supports:

Offline stores: Google BigQuery, Amazon Redshift, Amazon S3 (via Redshift)
Online stores: Google Cloud Firestore, Amazon DynamoDB, Redis, SQLite

Support for other stores such as Bigtable and Snowflake is on the roadmap. Feast can also easily be extended to support other stores.

Is Feast a database?

No. Feast is a tool that manages data stored in other systems (e.g. BigQuery, Cloud Firestore, Redshift, DynamoDB). It is not a database, but it helps manage data stored in other systems.

What clouds does Feast work on?

Feast is available today natively on GCP/AWS, and can easily extend to work in other clouds.

Does Feast support streaming data?

Not by default. Feature views support streaming sources, but custom configuration is needed. Improved support for streaming data is actively being developed.