Python SDK for Feast
Project description
Feast - Feature Store for Machine Learning
Overview
Feast (Feature Store) is a tool for managing and serving machine learning features. Feast is the bridge between models and data.
Feast aims to:
- Provide a unified means of managing feature data from a single person to large enterprises.
- Provide scalable and performant access to feature data when training and serving models.
- Provide consistent and point-in-time correct access to feature data.
- Enable discovery, documentation, and insights into your features.
TL;DR: Feast decouples feature engineering from feature usage. Features that are added to Feast become available immediately for training and serving. Models can retrieve the same features used in training from a low latency online store in production. This means that new ML projects start with a process of feature selection from a catalog instead of having to do feature engineering from scratch.
# Setting things up
fs = feast.Client('feast.example.com')
customer_features = ['CreditScore', 'Balance', 'Age', 'NumOfProducts', 'IsActive']
# Training your model (typically from a notebook or pipeline)
data = fs.get_batch_features(customer_features, customer_entities)
my_model = ml.fit(data)
# Serving predictions (when serving the model in production)
prediction = my_model.predict(fs.get_online_features(customer_features, customer_entities))
Important resources
Notice
Feast is a community project and is still under active development. Your feedback and contributions are important to us. Please have a look at our contributing guide for details.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for feast-0.4.1.post0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7a1d581751015db74a24688023fcbeba48e3468535d5e26fbf404e68e1ee7d0d |
|
MD5 | 9de3a149c79f8bfd3f9f2249e851cc9e |
|
BLAKE2b-256 | e989deb8df002f2eb03158d608525475bd749f1abbf401744af4379461fc51bc |