You recently deployed a scikit-learn model to a Vertex AI endpoint. You are now testing the model on live production traffic. While monitoring the endpoint, you discover twice as many requests per…

Deploying two models to the same endpoint for even distribution is typically used for A/B testing or redundancy, not for dynamic scaling to handle unforeseen surges in request volume.

`minReplicaCount` ensures a baseline number of replicas but does not configure the dynamic scaling behavior required to efficiently handle increases in demand beyond that minimum.

Vertex AI Endpoints utilize `autoscalingMetricSpecs` to define how they scale dynamically based on demand. To efficiently scale and prevent high latency during increased traffic, these specifications must be configured to ensure sufficient replicas are provisioned proactively, allowing the endpoint to efficiently handle demand surges and maintain low latency.

Changing the model's machine type or adding GPUs increases the capacity of *individual* replicas but does not directly address the horizontal scaling efficiency for handling a significantly higher *volume* of requests, and scikit-learn models generally do not benefit from GPUs for inference.