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🚦 Deployment Hyperparameters

A 3D model-serving cluster where replica count, batching, autoscaling and canary traffic split reshape request latency, throughput and queue depth live.

AI & Machine Learning3DAdvanced60 FPS

A live 3D model-serving cluster where a load balancer routes request traffic to a ring of replica pods — batching, autoscaling and canary traffic split all reshape latency, throughput and queue depth in real time.

🔬 What It Demonstrates

Batch size trades per-request latency for per-replica throughput; too few replicas under heavy traffic builds a queue and starts dropping requests; autoscaling reacts to that queue depth the way a production orchestrator would.

🎮 How to Use

Raise traffic and watch latency and queue depth respond. Adjust batch size and replica count to find the trade-off, or flip on autoscaling and let the cluster size itself. Route a slice of traffic to the teal canary pods to see a rollout in isolation.

💡 Did You Know?

Canary deployments get their name from "canary in a coal mine" — routing a small, monitored slice of live traffic to a new model version lets teams catch regressions before they reach every user.

⚙ Under the hood

A 3D model-serving cluster where replica count, batching, autoscaling and canary traffic split reshape request latency, throughput and queue depth live.

machine learningartificial intelligenceclusteringautoscalingbatchinghyperparametersThree.js

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