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☁️ Deploying Machine Learning Models to the Cloud

An interactive 3D cluster of model-serving pods behind a load balancer — watch autoscaling, request routing and rolling / canary / blue-green rollouts of a new model version.

AI & Machine Learning3DAdvanced60 FPS

A load balancer routes a stream of inference requests across a ring of model-serving pods. Watch the fleet autoscale with traffic and roll out a new model version using rolling, canary, or blue-green deployment.

🔬 What It Demonstrates

How request traffic maps to pod count under autoscaling, how estimated tail latency rises under load, and how three real deployment strategies mix old and new model versions differently while a rollout is in progress.

🎮 How to Use

Raise traffic to see the pod ring scale, pick a deployment strategy, then click Deploy v2 (or drag the canary weight slider) to send a new model version live and watch requests shift from blue pods to amber ones.

💡 Did You Know?

Canary releases are named after canaries once carried into coal mines to give miners early warning of danger — a canary deployment gives engineers the same early warning before a bad model reaches every user.

⚙ Under the hood

An interactive 3D cluster of model-serving pods behind a load balancer — watch autoscaling, request routing and rolling / canary / blue-green rollouts of a new model version.

machine learningcloud computingautoscalingmodel deploymentkubernetesload balancercanary releaseThree.js

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