Exam Professional Machine Learning Engineer topic 1 question 248 discussion

From StackOverflow:"Validate the container configuration port; it should use port 8080. This configuration is important because Vertex AI sends liveness checks, health checks, and prediction requests to this port on the container. " Pulling the container to the local machine is like stepping back and saying, "It works on my computer," then solving the problem as it arises.

Isolating the Issue: Running the container locally helps determine if the problem originates from the container configuration or the Vertex AI deployment environment. If the container runs successfully locally, the issue likely lies with Vertex AI. Detailed Error Messages: Examining the container logs using docker logs provides detailed error messages specific to the container startup process. These messages can pinpoint the root cause of the model server failure, such as missing dependencies, incorrect model format, or resource limitations.

I vote for D. Pull the Docker image locally, and use the docker run command to launch it locally. Use the docker logs command to explore the error logs. Here's why: 1. Local Testing by running the Docker image locally to replicate the environment the model server encounters within Vertex AI. 2. Using docker logs allows to inspect the detailed error messages generated by the model server during startup. These logs might provide specific clues about the cause of the "model server never became ready" error.

When deploying a custom container to Vertex AI Model Registry, need to follow some requirements for the container configuration. One of these requirements is to use the HTTP port 8080 forserving predictions. If using a different port, the model server might not be able to communicate with Vertex AI and cause the error “Error model server never became ready”. To fix this error, change the HTTP port in your model’s configuration to the default value of 8080 and redeploy the container.

My Answer: D A: Not correct: While logging can be helpful for monitoring and debugging, it won't directly address the issue of the model server not becoming ready. B: Not correct: The error message doesn't indicate a port issue, changing it preemptively might not resolve the underlying problem. C: Not correct: changing the health route, which could be helpful if the issue is related to health checks, but without further information, it's not the most conclusive option. D: CORRECT: This option allows you to simulate the deployment environment locally and inspect the logs directly, which can help diagnose the issue with the model server not becoming ready.

Due to Model size or other reasons so that it cannot pass health check before timeout. https://cloud.google.com/knowledge/kb/unable-to-deploy-a-large-model-into-a-vertex-endpoint-000010439

Validate the container configuration port, it should use port 8080. This configuration is important because Vertex AI sends liveness checks, health checks, and prediction requests to this port on the container. https://www.appsloveworld.com/coding/flask/15/vertex-ai-deployment-failed

when not specifying the health check, the endpoint uses a default health check which only indicates if the http server is ready, not if the model is ready. https://cloud.google.com/vertex-ai/docs/predictions/custom-container-requirements#health

Option A: Adding logging to Cloud Logging is useful for long-term monitoring but might not provide immediate insights for this specific error. Options B and C: Changing port and health check configuration might be necessary if incorrect, but local debugging often reveals the root cause more effectively.