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🗄️ Feature Stores: Scaling ML Past a Handful of Models

A 3D hub-and-spoke visualization comparing ad-hoc per-model feature pipelines against a shared feature store as the number of ML models in an organization grows.

AI & Machine Learning3DAdvanced60 FPS

A hub-and-spoke 3D diagram comparing ad-hoc, per-model feature pipelines against a shared feature store as an organization's model count grows from a handful to a dozen or more.

🔬 What It Demonstrates

Without a shared store, each model wires directly into every raw data source, so feature computations multiply as sources × models and training/serving definitions can silently drift apart. A feature store computes each feature once and serves it consistently everywhere.

🎮 How to Use

Increase the number of models to watch the spaghetti of direct pipelines grow. Toggle the shared feature store on to collapse it into a single hub, and toggle the skew highlight to see which ad-hoc pipelines risk training-serving mismatch.

💡 Did You Know?

Uber's Michelangelo platform, launched around 2017, is widely credited with popularizing the "feature store" as a distinct piece of ML infrastructure after duplicate, inconsistent feature logic became a recurring source of production bugs.

⚙ Under the hood

A 3D hub-and-spoke visualization comparing ad-hoc per-model feature pipelines against a shared feature store as the number of ML models in an organization grows.

machine learningfeature storesmodel scalingdata pipelinesml engineeringartificial intelligenceThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install