Professional Data EngineerOperationalizing machine learning modelsEasy
A financial institution uses a machine learning model to detect anomalies in transaction data. The model is deployed on Vertex AI Endpoints. Due to new regulatory requirements, the institution must be able to explain the predictions made by the model for any given transaction. Which Vertex AI feature should be integrated to meet this requirement?
- AVertex AI Model Monitoring
- BVertex AI Feature Store
- CVertex Explainable AI
- DVertex AI Workbench
Show answer & explanationAnswer & explanation
Correct answer: C. Vertex Explainable AI
Vertex Explainable AI is specifically designed to provide insights into how a model makes predictions, offering feature attributions for individual predictions, which is crucial for regulatory compliance and understanding model behavior.
Why the other options are wrong
- A. Vertex AI Model Monitoring tracks model performance and drift, not individual prediction explanations.
- B. Vertex AI Feature Store manages and serves features for training and serving, not for explaining predictions.
- D. Vertex AI Workbench provides Jupyter notebooks for ML development, not explanation for deployed models.
Vertex Explainable AI
A component of Vertex AI that helps users understand the output of their machine learning models by providing feature attributions for predictions.
- Enhances model transparency and interpretability.
- Supports various explanation methods (e.g., SHAP, LIME).
- Crucial for regulatory compliance and debugging.
Memory trick: Explainable AI sheds light on 'why' your model predicted what it did.