feature stores.

Where feature stores fits on a long engagement.

The organisational benefit is often larger than the technical one. A catalogue of documented, tested features means a new model starts from existing work rather than reimplementing the same customer aggregations for the fourth time, each slightly differently.

The technical benefit is guaranteed consistency. Because both paths read the same definition, training-serving skew is prevented structurally rather than by convention, which is the only way it stays prevented as a team changes.

What an assigned team does with feature stores.

The cost is real: another system, materialisation pipelines, an online store with availability requirements, and monitoring. A single model with a handful of features does not justify it.

The threshold is usually several models sharing features, or a skew incident that has already happened. Making that judgement with the client rather than proposing infrastructure is how scope is set, as described in how an assignment runs.

What we use feature stores for.

• Features reused rather than rebuilt A catalogue so the same aggregation is not reimplemented per project.
• Skew prevented structurally One definition read by both paths, rather than a convention that erodes.
• Adoption timed to the need Introduced when several models share features, not as a default architecture.

How feature stores capacity is assigned.

Feature platform work is assigned inside AI capacity, with adoption timed to actual sharing rather than anticipated need.