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Model Serving Architectures for Production ML (2D)

2D inference-cluster lab: tune request rate, batch size, replica count and autoscaling to watch queueing latency, throughput and queue depth respond in real time.

AI & Machine Learning2DModerate60 FPS📱 Mobile-adapted⇄ 3D version

This 2D companion runs the same queueing math as the 3D cluster on a flat canvas: particles stream from a client node through a load balancer to a row of replica boxes, each replica reddening as its load climbs. A latency sparkline along the top makes the classic utilization "hockey stick" easy to read at a glance.

⚙ Under the hood

2D inference-cluster lab: tune request rate, batch size, replica count and autoscaling to watch queueing latency, throughput and queue depth respond in real time.

model servinginferenceautoscalingdynamic batchingqueueing theorylatency

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install