# Feast Use Cases

## Recommendation Engines

Recommendation engines require personalized feature data related to users, items, and their interactions. Feast can help by:

- **Managing feature data**: Store and serve user preferences, item characteristics, and interaction history

- **Low-latency serving**: Provide real-time features for dynamic recommendations

- **Point-in-time correctness**: Ensure training and serving data are consistent to avoid data leakage

- **Feature reuse**: Allow different recommendation models to share the same feature definitions


### Example: User-Item Recommendations

A typical recommendation engine might need features such as:

- User features: demographics, preferences, historical behavior

- Item features: categories, attributes, popularity scores

- Interaction features: past user-item interactions, ratings


Feast allows you to define these features once and reuse them across different recommendation models, ensuring consistency between training and serving environments.

[Driver ranking](https://docs.feast.dev/tutorials/tutorials-overview/driver-ranking-with-feast)

## Risk Scorecards

Risk scorecards (such as credit risk, fraud risk, and marketing propensity models) require a comprehensive view of entity data with historical contexts. Feast helps by:

- **Feature consistency**: Ensure all models use the same feature definitions

- **Historical feature retrieval**: Generate training datasets with correct point-in-time feature values

- **Feature monitoring**: Track feature distributions to detect data drift

- **Governance**: Maintain an audit trail of features used in regulated environments


### Example: Credit Risk Scoring

Credit risk models might use features like:

- Transaction history patterns

- Account age and status

- Payment history features

- External credit bureau data

- Employment and income verification


Feast enables you to combine these features from disparate sources while maintaining data consistency and freshness.

[Real-time credit scoring on AWS](https://docs.feast.dev/tutorials/tutorials-overview/real-time-credit-scoring-on-aws) [Fraud detection on GCP](https://docs.feast.dev/tutorials/tutorials-overview/fraud-detection)

## NLP / RAG / Information Retrieval

Natural Language Processing (NLP) and Retrieval Augmented Generation (RAG) applications require efficient storage and retrieval of text embeddings. Feast supports these use cases by:

- **Vector storage**: Store and index embedding vectors for efficient similarity search

- **Document metadata**: Associate embeddings with metadata for contextualized retrieval

- **Scaling retrieval**: Serve vectors with low latency for real-time applications

- **Versioning**: Track changes to embedding models and document collections


### Example: Retrieval Augmented Generation

RAG systems can leverage Feast to:

- Store document embeddings and chunks in a vector database

- Retrieve contextually relevant documents for user queries

- Combine document retrieval with entity-specific features

- Scale to large document collections


Feast makes it remarkably easy to make data available for retrieval by providing a simple API for both storing and querying vector embeddings.

[Retrieval Augmented Generation (RAG) with Feast](https://docs.feast.dev/tutorials/rag-with-docling)

## Time Series Forecasting

Time series forecasting for demand planning, inventory management, and anomaly detection benefits from Feast through:

- **Temporal feature management**: Store and retrieve time-bound features

- **Feature engineering**: Create time-based aggregations and transformations

- **Consistent feature retrieval**: Ensure training and inference use the same feature definitions

- **Backfilling capabilities**: Generate historical features for model training


### Example: Demand Forecasting

Demand forecasting applications typically use features such as:

- Historical sales data with temporal patterns

- Seasonal indicators and holiday flags

- Weather data

- Price changes and promotions

- External economic indicators


Feast allows you to combine these diverse data sources and make them available for both batch training and online inference.

## Image and Multi-Modal Processing

While Feast was initially built for structured data, it can also support multi-modal applications by:

- **Storing feature metadata**: Keep track of image paths, embeddings, and metadata

- **Vector embeddings**: Store image embeddings for similarity search

- **Feature fusion**: Combine image features with structured data features


## Why Feast Is Impactful

Across all these use cases, Feast provides several core benefits:

1. **Consistency between training and serving**: Eliminate training-serving skew by using the same feature definitions

2. **Feature reuse**: Define features once and use them across multiple models

3. **Scalable feature serving**: Serve features at low latency for production applications

4. **Feature governance**: Maintain a central registry of feature definitions with metadata

5. **Data freshness**: Keep online features up-to-date with batch and streaming ingestion

6. **Reduced operational complexity**: Standardize feature access patterns across models


By implementing a feature store with Feast, teams can focus on model development rather than data engineering challenges, accelerating the delivery of ML applications to production.

- [Feast Use Cases](https://docs.feast.dev/getting-started/use-cases)