feast

Pre-release

This release is a pre-release and may not be stable for production use.

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Overview

Feast (Feature Store) is an open source feature store for machine learning. Feast is the fastest path to manage existing infrastructure to productionize analytic data for model training and online inference.

Feast allows ML platform teams to:

Make features consistently available for training and serving by managing an offline store (to process historical data for scale-out batch scoring or model training), a low-latency online store (to power real-time prediction), and a battle-tested feature server (to serve pre-computed features online).
Avoid data leakage by generating point-in-time correct feature sets so data scientists can focus on feature engineering rather than debugging error-prone dataset joining logic. This ensure that future feature values do not leak to models during training.
Decouple ML from data infrastructure by providing a single data access layer that abstracts feature storage from feature retrieval, ensuring models remain portable as you move from training models to serving models, from batch models to realtime models, and from one data infra system to another.

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 Snowflake/GCP/AWS? Click here.

🐣 Getting Started

1. Install Feast

pip install feast

2. Create a feature repository

feast init my_feature_repo
cd my_feature_repo/feature_repo

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

feast apply

4. Explore your data in the web UI (experimental)

Web UI

feast ui

5. 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,
    features = [
        '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  conv_rate  acc_rate  avg_daily_trips
0 2021-04-12 08:12:10+00:00       1002   0.713465  0.597095              531
1 2021-04-12 10:59:42+00:00       1001   0.072752  0.044344               11
2 2021-04-12 15:01:12+00:00       1004   0.658182  0.079150              220
3 2021-04-12 16:40:26+00:00       1003   0.162092  0.309035              959

6. Load feature values into your online store

Option 1: Incremental materialization (recommended)

CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize-incremental $CURRENT_TIME

Option 2: Full materialization with timestamps

CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize 2021-04-12T00:00:00 $CURRENT_TIME

Option 3: Simple materialization without timestamps

feast materialize --disable-event-timestamp

The --disable-event-timestamp flag allows you to materialize all available data (from 1970-01-01 up to the current UTC time) without specifying start and end timestamps. Rows keep the event timestamps from the source.

Materializing feature view driver_hourly_stats from 2021-04-14 to 2021-04-15 done!

7. Read online features at low latency

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)

# 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]
}

📦 Functionality and Roadmap

The list below contains the functionality that contributors are planning to develop for Feast.

We welcome contribution to all items in the roadmap!
Natural Language Processing
- Vector Search (Alpha release. See RFC)
- Enhanced Feature Server and SDK for native support for NLP
Data Sources
Offline Stores
Online Stores
Feature Engineering
- On-demand Transformations (On Read) (Beta release. See RFC)
- Streaming Transformations (Alpha release. See RFC)
- Batch transformation (In progress. See RFC)
- On-demand Transformations (On Write) (Beta release. See GitHub Issue)
Streaming
Deployments
- AWS Lambda (Alpha release. See RFC)
- Kubernetes (See guide)
Feature Serving
Data Quality Management (See RFC)
- Feature Quality Monitoring — built-in metrics, drift detection, serving log monitoring, and UI dashboard
Feature Discovery and Governance
- Python SDK for browsing feature registry
- CLI for browsing feature registry
- Model-centric feature tracking (feature services)
- Amundsen integration (see Feast extractor)
- DataHub integration (see DataHub Feast docs)
- Feast Web UI (Beta release. See docs)
- Feast Lineage Explorer

🎓 Important Resources

Please refer to the official documentation at Documentation

👋 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:

🌟 GitHub Star History

✨ Contributors

Thanks goes to these incredible people:

aerospike aws azure cassandra clickhouse couchbase delta docling duckdb elasticsearch faiss flink gcp ge glide go grpcio remote hazelcast hbase ibis k8s image milvus mongodb mssql oracle mysql openlineage opentelemetry spark iceberg lance trino postgres postgres-c pytorch qdrant rag ray redis scylladb singlestore snowflake sqlite-vec mcp mcp-server mlflow dbt test ci nlp dev docs minimal setuptools

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