A PyTorch model deployed on nl-highcpu-16 machines in us-central1 region of Google Cloud exhibits high latency, particularly in Singapore. The model classifies transactions as fraudulent or not and uses numerical and categorical features.
Several solutions are proposed:
The suggested answer is C, deploying the model to Vertex AI private endpoints in both the US and Singapore regions to minimize latency.
You work for a large bank that serves customers through an application hosted in Google Cloud that is running in the US and Singapore. You have developed a PyTorch model to classify transactions as potentially fraudulent or not. The model is a three-layer perceptron that uses both numerical and categorical features as input, and hashing happens within the model.
You deployed the model to the us-central1 region on nl-highcpu-16 machines, and predictions are served in real time. The model's current median response latency is 40 ms. You want to reduce latency, especially in Singapore, where some customers are experiencing the longest delays. What should you do?
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