You trained a model packaged it with a custom Docker container for serving, and deployed it to Vertex AI Model Registry. When you submit a batch prediction job, it fails with this error: "Error model

While adding logging is good practice, the problem states "no additional errors in the logs," implying existing logging isn't catching the startup failure, or the container isn't even reaching the point where application logs are emitted.

The default HTTP port on Vertex AI for custom containers is often 8080, so changing it might not fix the issue if it's already correct, and it's not the primary debugging step when logs are absent.

While `/healthcheck` is a common health route, the specific route used depends on the container's implementation, and blindly changing it without local debugging won't solve an underlying startup failure.

The error "model server never became ready" indicates a problem during the container startup or the model server initialization within the container. Since there are no additional errors in the logs, running the Docker image locally allows you to directly interact with the container, inspect its internal logs, check port bindings, and test the health checks and prediction endpoints in a controlled environment to diagnose the root cause.