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.
The above architecture is the minimal Feast deployment. Want to run the full Feast on Snowflake/GCP/AWS? Click here.
pip install feast
feast init my_feature_repo
cd my_feature_repo
feast apply
feast ui
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
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!
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]
}The list below contains the functionality that contributors are planning to develop for Feast
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Items below that are in development (or planned for development) will be indicated in parentheses.
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We welcome contribution to all items in the roadmap!
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Want to influence our roadmap and prioritization? Submit your feedback to this form.
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Want to speak to a Feast contributor? We are more than happy to jump on a call. Please schedule a time using Calendly.
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Data Sources
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Offline Stores
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Online Stores
- DynamoDB
- Redis
- Datastore
- SQLite
- Azure Cache for Redis (community plugin)
- Postgres (contrib plugin)
- Custom online store support
- Bigtable (in progress)
- Cassandra
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Streaming
- Custom streaming ingestion job support
- Push based streaming data ingestion
- Streaming ingestion on AWS
- Streaming ingestion on GCP
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Feature Engineering
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Deployments
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Feature Serving
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Data Quality Management (See RFC)
- Data profiling and validation (Great Expectations)
- Training-serving skew detection (in progress)
- Metric production
- Drift detection
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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)
- Feast Web UI (Alpha release. See documentation)
- REST API for browsing feature registry
- Feature versioning
Please refer to the official documentation at Documentation
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
Thanks goes to these incredible people: