Exam Professional Machine Learning Engineer topic 1 question 137 discussion

Option D is the best approach to determine how often to retrain the model while minimizing cost. Running training-serving skew detection batch jobs every few days to compare the aggregate statistics of the features in the training dataset with recent serving data is an effective way to detect when the model's performance has degraded. If skew is detected, the most recent serving data should be sent to the labeling service to evaluate the model's performance. This approach is more cost-effective than sending a subset of requests to the labeling service every month because it only sends data when there is a high probability that the model's performance has degraded. By doing this, the model can be retrained at the right time, and the cost of the labeling service can be minimized.

Clearly D. B is just guesswork.

Went with D

D https://cloud.google.com/blog/topics/developers-practitioners/monitor-models-training-serving-skew-vertex-aiew-vertex-ai&ved=2ahUKEwiRg_aoj9n8AhWb7TgGHcGCDREQFnoECAwQAQ&usg=AOvVaw197NneIJM0ra7fLq2zsOin

B looks the only option, to me.

D - https://cloud.google.com/blog/topics/developers-practitioners/monitor-models-training-serving-skew-vertex-ai - https://developers.google.com/machine-learning/guides/rules-of-ml

I think D

"After a year, you notice that your model's performance sometimes degrades significantly after a month, while other times it takes several months to notice any decrease in performance." Hence I vote B