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Question 217 - Professional Machine Learning Engineer discussion

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You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex Al Model Registry. When you submit a batch prediction job, it fails with this error 'Error model server never became ready Please validate that your model file or container configuration are valid. There are no additional errors in the logs What should you do?

A.
Add a logging configuration to your application to emit logs to Cloud Logging.
Answers
A.
Add a logging configuration to your application to emit logs to Cloud Logging.
B.
Change the HTTP port in your model's configuration to the default value of 8080
Answers
B.
Change the HTTP port in your model's configuration to the default value of 8080
C.
Change the health Route value in your models configuration to /heal thcheck.
Answers
C.
Change the health Route value in your models configuration to /heal thcheck.
D.
Pull the Docker image locally and use the decker run command to launch it locally. Use the docker logs command to explore the error logs.
Answers
D.
Pull the Docker image locally and use the decker run command to launch it locally. Use the docker logs command to explore the error logs.
Suggested answer: B

Explanation:

When you deploy a custom container to Vertex AI Model Registry, you need to follow some requirements for the container configuration. One of these requirements is to use the HTTP port 8080 for serving predictions. If you use 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, you need to change the HTTP port in your model's configuration to the default value of 8080 and redeploy the container.Reference:

Custom container requirements documentation

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

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asked 18/09/2024
Melissa Petrini
29 questions
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