Exam Professional Machine Learning Engineer topic 1 question 247 discussion

D. You want to control how much the distribution of the data changes over time -> that's drift.

the answer cannot be C, cause your training data is not available in production. So D is the only viable answer

Option D is incorrect in my opinion. "Feature attribution skew and drift detection" focus on knowing how feature values are contributing to the model’s predictions, but "training-serving skew" is more direct and efficient to detect distribution changes that could lead to performance issues

D, as the training data is not available

Security: Vertex AI Model Monitoring doesn't require uploading your training data to the cloud. It analyzes model predictions and input features on your on-premises server. Data Distribution Shifts: Feature attribution techniques like LIME or SHAP within Vertex AI Model Monitoring can identify how different features contribute to model predictions. Detecting drifts in these feature attributions can indicate changes in the underlying data distribution compared to the training data.

Feature Attribution Skew and Drift Detection, this type of monitoring is useful in some cases, it requires access to the training and serving data for analysis. Since data cannot move to the cloud, Option D wouldn't be feasible. I vote for C. Create a Vertex AI Model Monitoring job. Enable training-serving skew detection for your model.

the answer cannot be C, cause your training data is not available in production.

Option A and B: Vertex Explainable AI provides insights into model behavior but doesn't directly detect performance changes or concept drift. It's more suitable for understanding model decisions, not monitoring production performance. Option D: Feature attribution skew and drift detection requires feature attributions calculated during training, which might not be feasible without cloud access to the data.