Exam Professional Machine Learning Engineer topic 1 question 245 discussion

Since the goal is to minimize cost while maintaining accuracy, Option D provides a more targeted approach for retraining based on the likelihood of the model being outdated due to data changes. Option B might trigger retraining more frequently even if the performance difference doesn't necessarily stem from a significant shift in the data distribution.

My answer D: A and C: Not Correct: Schedule a retrain every night is not necessary since the model is performing well. B. Not Correct: This approach focuses on internal consistency within the current training run, train versus loss evaluation. Comparing similar training and validation losses doesn't guarantee better performance than previous models. This is an approach to identity overfitting, for example, or model quality. D. Correct: This approach focuses on identifying performance changes over time. Comparing to previous runs helps assess if the new model performs better than the old one on the evaluation set. we will check if this new version is better or not than the old one https://www.youtube.com/watch?v=1ykDWsnL2LE&ab_channel=GoogleCloudTech

D. Compare the results to the evaluation results from a previous run. If the performance improved, deploy the model to a Vertex AI endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered, redeploy the pipeline.

Option A: Redeploying the pipeline every night without checking for degradation wastes resources if model performance is stable. Option C: Comparing results to a previous run doesn't guarantee model degradation detection in the current run. Option D: Comparing to a previous run and using model monitoring is redundant; model monitoring alone is sufficient.