Dee-Pac/feast

Feature Store for Machine Learning

Overview

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.

📐 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

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

feast apply

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

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

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!

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

• 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

  • Snowflake source
  • Redshift source
  • BigQuery source
  • Parquet file source
  • Synapse source (community plugin)
  • Hive (community plugin)
  • Postgres (community plugin)
  • Spark (community plugin)
  • Kafka source (with push support into the online store)
  • HTTP source

• Offline Stores

  • Snowflake
  • Redshift
  • BigQuery
  • Synapse (community plugin)
  • Hive (community plugin)
  • Postgres (community plugin)
  • Trino (community plugin)
  • Spark (community plugin)
  • In-memory / Pandas
  • Custom offline store support

• Online Stores

  • DynamoDB
  • Redis
  • Datastore
  • SQLite
  • Azure Cache for Redis (community plugin)
  • Postgres (community plugin)
  • Custom online store support
  • Bigtable (in progress)
  • Cassandra

• Streaming

  • Custom streaming ingestion job support
  • Push based streaming data ingestion
  • Streaming ingestion on AWS
  • Streaming ingestion on GCP

• Feature Engineering

  • On-demand Transformations (Alpha release. See RFC)
  • Batch transformation (SQL. In progress. See RFC)
  • Streaming transformation

• Deployments

  • AWS Lambda (Alpha release. See RFC)
  • Kubernetes (See guide)
  • Cloud Run
  • KNative

• Feature Serving

  • Python Client
  • REST Feature Server (Python) (Alpha release. See RFC)
  • gRPC Feature Server (Java) (See #1497)
  • Push API
  • Java Client
  • Go Client
  • Delete API
  • Feature Logging (for training)

• Data Quality Management (See RFC)

  • Data profiling and validation (Great Expectations)
  • Training-serving skew detection (in progress)
  • Metric production
  • Drift detection

• 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 (in progress)
  • REST API for browsing feature registry
  • Feature versioning

🎓 Important Resources

Please refer to the official documentation at Documentation

• Quickstart
• Tutorials
• Running Feast with Snowflake/GCP/AWS
• Change Log
• Slack (#Feast)

👋 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:

• Contribution Process for Feast
• Development Guide for Feast
• Development Guide for the Main Feast Repository

✨ Contributors

Thanks goes to these incredible people: