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A data scientist wants to test a new model version on a small percentage of traffic before full rollout. Which Vertex AI feature allows this?
Vertex AI Endpoint traffic splitting allows you to route a specified percentage of inference requests to different model versions deployed on the same endpoint. This enables gradual rollout by directing a small fraction of traffic (e.g., 5%) to the new model while the rest goes t…Read full explanation
A machine learning team wants to deploy a new model version for canary testing, where only 5% of traffic is routed to the new version. Which Vertex AI endpoint configuration supports this?
Vertex AI endpoints natively support traffic splitting, allowing you to route a specified percentage of requests to different model versions deployed on the same endpoint. By configuring a traffic split of 95% to the old version and 5% to the new version, you can perform canary t…Read full explanation
Which TWO actions can help reduce prediction latency for a Vertex AI endpoint?
Optimizing the model architecture to reduce size directly decreases the computational load during inference, which lowers prediction latency. Smaller models require fewer floating-point operations (FLOPs) per prediction, enabling faster response times on Vertex AI endpoints.
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