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GeezerVison is showing feast.dev — Feast - The Open Source Feature Store for Machine Learning

Serving Data 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]

293

Contributors

12M+

Downloads

5.5K

Slack Members

[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

IKV

[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()

# Retrieve your documents using vector similarity search for RAG
features = store.retrieve_online_documents(
    features=[
        "corpus:document_id",
        "corpus:chunk_id",
        "corpus:chunk_text",
        "corpus:chunk_embedding",
    ],
    query="What is the biggest city in the USA?"
).to_dict()

[BLOG POSTS]

THE LATEST FROM FEAST

Blog

Bringing Chronon Features to Feast

Use Feast to discover and retrieve Chronon features for training and inference, while Chronon owns computation and serving.

Blog

Feast Feature and Vector Serving at ScyllaDB Speed

Starting with Feast v0.65, a new tighter integration between Feast and ScyllaDB brings vector search support and better performance to the online store. This post covers what changed, why teams outgrow their existing online store, why ScyllaDB fits feature store workloads, and how to configure it in Feast.

Blog

Fast like a cache, priced like storage: Benchmarking Aerospike on Feast

We ran Feast's benchmark harness against Aerospike and compared it with the published Redis and DynamoDB results, sweeping entities per request, features per request, and request rate.

[GET STARTED]

START BUILDING TODAY

Join our Slack

Become part of our developer community & get support from the Feast developers

Join Community

Docs

See our comprehensive documentation and start building with Feast today

Read Docs

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