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🚀 MLOps Fundamentals: Running ML in Production

An interactive 3D MLOps pipeline: watch data flow through training, a model registry and a canary/production split, then drift and trigger automated retraining.

AI & Machine Learning3DAdvanced60 FPS

A 3D MLOps pipeline where data flows from a data lake through training into a versioned model registry, splits across a canary and production deployment, and gets watched by a live drift monitor.

🔬 What It Demonstrates

How production ML systems are more than a trained model: versioned training runs, staged canary rollout, live serving traffic, and continuous drift monitoring that can trigger automatic retraining.

🎮 How to Use

Set the drift rate, traffic volume and canary split, then watch accuracy fall as drift rises. Leave auto-retrain on to see the pipeline recover itself, or force a retrain manually at any time.

💡 Did You Know?

Most production model failures aren't bugs in the code — they're silent data or concept drift, which is why monitoring dashboards and scheduled retraining pipelines are considered core MLOps infrastructure.

⚙ Under the hood

An interactive 3D MLOps pipeline: watch data flow through training, a model registry and a canary/production split, then drift and trigger automated retraining.

machine learningproduction mlmlopspipelineautomationmodel deploymentThree.js

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