Engineering, production, governance, cost, communication · insight-0301

Adopt a feature store when features are shared or served online, not by default

Situation
A team with two batch models plans to deploy a feature store as its first MLOps step.
Action
List the actual problems: online serving needs, features reused across teams, point-in-time training joins, skew incidents. Adopt a feature store when several apply; otherwise use versioned tables and shared feature code.
Reason
Feature stores address point-in-time joins and consistent online and offline access; without those needs, simpler tools suffice.
Boundary
Platform decisions for growing ML teams.

Symptom or risk

Platform work delays value, and the store adds infrastructure to operate while solving problems the team does not yet have.

Organisations with an existing platform team and store can onboard small teams cheaply.

Delaying a store may mean migrating features later; design table conventions to ease that.

Choosing the store before defining feature ownership and naming.

Feast point-in-time joins retrieve feature values as of each entity row's timestamp for training data.
Point-in-time joins (Feast documentation)