Feast (Feature Store) is a tool to manage storage and access of machine learning features. It aims to:

• Support ingesting feature data via batch or streaming
• Provide scalable storage of feature data for serving and training
• Provide an API for low latency access of features
• Enable discovery and documentation of features
• Provide an overview of the general health of features in the system

This Python library allows you to register features and entities in Feast, ingest feature values, and retrieve the values for model training and serving.

Install feast library using pip:

 pip install feast

Make sure you have a running Feast instance. If not, follow this installation guide

All interaction with feast cluster happens via an instance of feast.sdk.client.Client. The client should be pointed to correct core/serving URL of the feast cluster

from feast.client import Client

# Assuming you are running Feast locally so Feast hostname is localhost
FEAST_CORE_URL="localhost:50051"
FEAST_SERVING_URL="localhost:50052"

feast_client = Client(
    core_url=FEAST_CORE_URL, 
    serving_url=FEAST_SERVING_URL)

from feast.feature_set import FeatureSet
from feast.entity import Entity
from feast.feature_set import Feature

fs1 = FeatureSet("my-feature-set-1")
fs1.add(Feature(name="fs1-my-feature-1", dtype=ValueType.INT64))
fs1.add(Feature(name="fs1-my-feature-2", dtype=ValueType.STRING))
fs1.add(Entity(name="fs1-my-entity-1", dtype=ValueType.INT64))

# Register Feature Set with Core
client.apply(fs1)

feature_sets = client.list_feature_sets()

# TODO