Question

Your team trained several models in different frameworks (a TensorFlow SavedModel, a PyTorch model exported to TorchScript, and an ONNX classifier). You want to serve all of them behind a single high-performance inference server on a Vertex AI endpoint with GPUs, minimizing custom container maintenance while supporting multiple frameworks and features like concurrent model execution. Which approach best meets these needs?

Accepted Answer

Use the NVIDIA Triton Inference Server container on Vertex AI, placing all models in a shared model repository.

Answer

Deploy each model with a separate framework-specific prebuilt container and route requests across three endpoints.

Answer

Convert every model to TensorFlow SavedModel format and serve them all with the TensorFlow Serving prebuilt container.

Answer

Write a custom Flask container that loads each framework's runtime and dispatches requests based on a model name parameter.