# Use Cases

This page covers common use cases for Feast and how a feature store can benefit your AI/ML workflows.

## 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.

{% content-ref url="../tutorials/tutorials-overview/driver-ranking-with-feast" %}
[driver-ranking-with-feast](https://docs.feast.dev/tutorials/tutorials-overview/driver-ranking-with-feast)
{% endcontent-ref %}

## 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.

{% content-ref url="../tutorials/tutorials-overview/real-time-credit-scoring-on-aws" %}
[real-time-credit-scoring-on-aws](https://docs.feast.dev/tutorials/tutorials-overview/real-time-credit-scoring-on-aws)
{% endcontent-ref %}

{% content-ref url="../tutorials/tutorials-overview/fraud-detection" %}
[fraud-detection](https://docs.feast.dev/tutorials/tutorials-overview/fraud-detection)
{% endcontent-ref %}

## 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.

{% content-ref url="../tutorials/rag-with-docling" %}
[rag-with-docling](https://docs.feast.dev/tutorials/rag-with-docling)
{% endcontent-ref %}

## 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.