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IJONIS
Data & Infrastructure

Feature Store

A feature store is a central system for managing, storing, and serving ML features — the processed data points that serve as input for machine learning models. It ensures the same features are used consistently for training and inference, and avoids redundant feature computations across teams and projects.

Why does this matter?

Without a feature store, each ML team computes its own features — with inconsistencies, duplicated effort, and the risk that training and production use different data (training-serving skew). A feature store saves 30-50% of ML engineering time and makes ML models more reliable and reproducible.

How IJONIS uses this

We implement feature stores with Feast or Tecton — depending on infrastructure and real-time requirements. For mid-sized businesses, we often start with a lean Feast setup on PostgreSQL and scale as needed. Every feature is tagged with metadata, lineage, and quality indicators.

Frequently Asked Questions

When does my company need a feature store?
Once you have more than two ML models in production or different teams working on ML projects. Even with a single model, a feature store is worthwhile when training-serving skew is a risk — i.e., when prediction quality in production significantly deviates from test results.
What is training-serving skew and how does a feature store prevent it?
Training-serving skew occurs when features are computed differently in training than in production — for example, through different time windows or normalizations. A feature store ensures exactly the same computation logic is used for training and inference, eliminating this common error source.
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