# Feature Store Pattern

> Centralizes feature computation and storage for reuse across applications.

- **Category**: Data Science
- **Subcategory**: MLOps
- **Canonical URL**: https://designpattern.fyi/patterns/feature_store/

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## Description
**Context**: Multiple teams building duplicate pipelines to process raw data leads to inefficiency. The feature store pattern processes data once and stores it in a tabular format for everyone to use.


## Use Cases
When multiple ML applications need the same features or when you need feature consistency across training and serving.



## Implementation Example

```python
# Feature Store Example
feature_store.save("user_features", computed_features)
features = feature_store.get("user_features", user_id)
```



## Trade-offs


### Advantages

- - Feature reuse across applications

- - Consistency between training and serving

- - Reduces duplicate effort and compute




### Considerations & Drawbacks

- - Additional infrastructure to maintain

- - Data staleness concerns







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**Reference**: [Original Source](https://eugeneyan.com/writing/more-patterns/)