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 GCP/AWS? Click here.
pip install feast
feast init my_feature_repo
cd my_feature_repo
feast apply
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
-
Items below that are in development (or planned for development) will be indicated in parentheses.
-
We welcome contribution to all items in the roadmap!
-
Want to influence our roadmap and prioritization? Submit your feedback to this form.
-
Want to speak to a Feast contributor? We are more than happy to jump on a call. Please schedule a time using Calendly.
-
Data Sources
- Redshift source
- BigQuery source
- Parquet file source
- Kafka source (Planned for Q4 2021)
- HTTP source
- Snowflake source
- Synapse source
-
Offline Stores
-
Online Stores
- DynamoDB
- Redis
- Datastore
- SQLite
- Custom online store support
- Postgres
- Bigtable
- Cassandra
-
Streaming
- Custom streaming ingestion job support
- Streaming ingestion on AWS (Planned for Q4 2021)
- Streaming ingestion on GCP
-
Feature Engineering
- On-demand Transformations (Development in progress. See RFC)
- Batch transformation (SQL)
- Streaming transformation
-
Deployments
- AWS Lambda (Development in progress. See RFC)
- Cloud Run
- Kubernetes
- KNative
-
Feature Serving
-
Data Quality Management
- Data profiling and validation (Great Expectations) (Planned for Q4 2021)
- Metric production
- Training-serving skew detection
- Drift detection
- Alerting
-
Feature Discovery and Governance
- Python SDK for browsing feature registry
- CLI for browsing feature registry
- Model-centric feature tracking (feature services)
- REST API for browsing feature registry
- Feast Web UI (Planned for Q4 2021)
- Feature versioning
- Amundsen integration
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: