Feature Store
A centralized repository for storing, managing, and serving machine learning features. It ensures consistent feature computation between training and serving, and enables feature reuse across teams.
Why It Matters
Feature stores prevent the #1 cause of ML production failures: training-serving skew (features computed differently in training vs production). They also accelerate development through reuse.
Example
A feature store containing pre-computed features like 'customer_30day_purchase_count' and 'average_transaction_amount' that multiple teams use across fraud detection, recommendation, and churn models.
Think of it like...
Like a shared pantry in a co-working kitchen — common ingredients are prepared once and stored centrally so every cook can use them consistently without duplicating work.
Related Terms
Data Pipeline
An automated workflow that extracts data from sources, transforms it through processing steps, and loads it into a destination for use. In ML, data pipelines ensure consistent data flow from raw sources to model training.
Feature Engineering
The process of selecting, transforming, and creating input variables (features) from raw data to improve model performance. It requires domain knowledge to identify what information is most useful for the model.
MLOps
Machine Learning Operations — the set of practices that combine ML, DevOps, and data engineering to deploy and maintain ML models in production reliably and efficiently.
Training-Serving Skew
A discrepancy between how features are computed during model training versus how they are computed during production serving. This is one of the most common and hardest-to-detect causes of model failure.
Data Engineering
The practice of designing, building, and maintaining the systems and infrastructure that collect, store, and prepare data for analysis and machine learning.