Troubleshooting ML model deployment issues.
Question
After successfully training your ML model, you have successfully deployed it as a real-time service to an AKS inference environment.
During the live operation, you experience an error and your service crashes when you post data to the scoring endpoint.
Which is the best way to investigate the cause of the problem?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect becauseincluding statements to return error messages from the run() function should only be used for debugging purposes.
For security and performance reasons, this should be avoided in a production environment.
Try debugging errors in a local container environment.
Option B is CORRECT because including statements to return error messages from the run() function should only be used for debugging purposes.
Debugging errors should be done in a local container environment, hence this is the correct answer.
Option C is incorrect because inference errors can be caught within the run() function.
init() is not the right place for that.
In addition, for security reasons, returning detailed error messages should be avoided in production environments.
Option D is incorrect because inference errors can be caught within the run() function.
init() is not the right place for that.
Reference:
The best way to investigate the cause of the problem when a deployed machine learning (ML) model crashes during live operation is to add an error catching statement to the run() function in the development (DEV) environment. This will allow you to capture and return a detailed error message when an error occurs, which can help you diagnose the issue and determine the root cause.
Option A suggests adding an error catching statement to the run() function in both the production (PROD) and DEV environments. While this may be useful for diagnosing the issue in the PROD environment, it is not necessary and can potentially impact performance. Additionally, it is best practice to minimize the amount of debugging code in production environments to reduce the risk of introducing new issues or security vulnerabilities.
Option C suggests adding an error catching statement to the init() function in both the PROD and DEV environments. However, the init() function is only executed when the container starts up, and any errors that occur during runtime will not be captured by this approach. Therefore, it is not the most effective way to diagnose issues during live operation.
Option D suggests adding an error catching statement to the init() function in the DEV environment. However, as mentioned above, this approach will not capture errors that occur during runtime in the production environment.
Overall, adding an error catching statement to the run() function in the DEV environment is the best approach to diagnose issues during live operation because it captures errors that occur at runtime without impacting the production environment. Once the issue has been diagnosed, it may be necessary to update the code and redeploy the model to the production environment to resolve the issue.
