Feature Stores

Serve consistent ML features in training and production inference.

Maturity level: L3 — Can Deploy

Six perspectives on Feature Stores

Roadmap

Advanced MLOps — after MLflow and model serving basics.

Architecture

Feature layer between data warehouse and inference.

Company

Platform and senior MLOps roles at scale.

Projects

Capstone with online + offline features.

Interview

Feature engineering at scale.

Career

Senior MLOps / platform differentiator.

What & Why

What: Centralized store for ML features with offline and online serving layers.

Why: Train/serve skew is a top production ML failure — feature stores fix it.

Build this

Build a Feast feature store backing a real-time recommendation API.

Production reality

  • ! Stale features
  • ! Schema evolution
  • ! Latency SLO misses
  • ! Backfill failures

Interview preparation

  • What is train/serve skew?
  • When do you need a feature store?

Connected skills

MLflowKServeSQL

Explore Feature Stores in the interactive universe or train with live cohorts.

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