Feature Serving for Production AI
Feast is an open source feature store that delivers structured data to AI and LLM applications at high scale during training and inference
[ADOPTERS AND CONTRIBUTORS]
[USE CASES]
SOLVE REAL PROBLEMS
Real-Time Recommendations
Serve personalized product and content recommendations with real-time user interaction features
Fraud Detection
Detect fraudulent transactions using historical patterns and real-time behavioral features
Risk Scoring
Calculate risk scores for financial services using consistent features across training and inference
Customer Segmentation
Create dynamic customer segments using consistent feature definitions across teams
[INTEGRATIONS]
CONNECT WITH YOUR STACK
OFFLINE STORES
ONLINE STORES
[GET STARTED]
START SERVING IN SECONDS
from feast import FeatureStore
# Initialize the feature store
store = FeatureStore(repo_path="feature_repo")
# Get features for training
training_df = store.get_historical_features(
entity_df=training_entities,
features=[
"customer_stats:daily_transactions",
"customer_stats:lifetime_value",
"product_features:price"
]
).to_df()
# Get online features for inference
features = store.get_online_features(
features=[
"customer_stats:daily_transactions",
"customer_stats:lifetime_value",
"product_features:price"
],
entity_rows=[{"customer_id": "C123", "product_id": "P456"}]
).to_dict()[BLOG POSTS]
THE LATEST FROM FEAST
The Road to Feast 1.0
Exploring the goals and vision for Feast 1.0, including tighter Kubeflow integration, enterprise features, and graduation from LF AI and Data Foundation Incubation.
The Future of Feast
A look at Feast's journey, its evolution as a feature store, and the exciting path ahead with new maintainers and community-driven development.
What is a Feature Store?
A comprehensive guide to feature stores, their role in machine learning operations, and how they solve key challenges in the ML lifecycle.
[GET STARTED]
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