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Option A is incorrect because Cloud SQL is a relational database service that is not designed for storing and comparing model performance statistics. It would require writing complex SQL queries to perform the comparison, and it would not provide any visualization or analysis tools.

Option B is incorrect because Vertex AI does not support creating versions of models for each season per year. Vertex AI models are versioned based on the training data and hyperparameters, not on external factors such as seasonality or holidays. Moreover, the Evaluate tab of the Vertex AI UI only shows the performance metrics of a single model version, not across multiple versions.

Option C is incorrect because Kubeflow is a different platform than Vertex AI, and it does not integrate well with Vertex AI Pipelines. Kubeflow experiments are used to group pipeline runs that share a common goal or objective, not to compare performance statistics across different seasons or years. Kubeflow UI does not provide any tools to compare the results across the experiments, and it would require switching between different platforms to access the data.

Option D is correct because Vertex ML Metadata is a service that allows storing and tracking metadata associated with machine learning workflows, such as models, datasets, metrics, and events. Events are user-defined labels that can be used to group or slice the metadata for analysis. By using seasons and years as events, you can easily store and compare the performance statistics of each version of your models across different time periods. Vertex ML Metadata also provides tools to visualize and analyze the metadata, such as the ML Metadata Explorer and the What-If Tool.

To develop a credit risk model that meets the regulatory requirements and can be monitored over time, you should follow these steps:

Use Vertex Explainable AI with the sampled Shapley method.  Vertex Explainable AI is a service that provides feature attributions for machine learning models, which can help you understand how each feature contributes to the prediction 1 .  The sampled Shapley method is a technique that estimates the Shapley values for each feature, which are based on the marginal contribution of each feature to the prediction across all possible feature subsets 2 .  The sampled Shapley method is suitable for neural networks and other complex models, as it can capture the non-linear and interaction effects of the features 3 .

Enable Vertex AI Model Monitoring to check for feature distribution drift.  Vertex AI Model Monitoring is a service that helps you track and manage the performance and quality of your deployed models over time 4 . Feature distribution drift is a type of data drift that occurs when the distribution of the input features changes significantly from the training data, which can affect the model accuracy and reliability. By checking for feature distribution drift, you can detect when your model needs to be retrained or updated with new data.

Vertex AI Pipelines (based on Kubeflow) provides Google Cloud Pipeline Components (GCPC) . These are pre-built, optimized components that perform common tasks.

Pre-built Components: ModelUploadOp and ModelDeployOp are native components specifically designed for these two steps. Chaining them together in your pipeline definition is the " simplest " approach because you don ' t have to write any custom Docker containers or manage Python client logic manually.

Why other options are incorrect:

Options A and B: These require writing custom code and maintaining container images, which increases complexity and maintenance overhead compared to using the built-in operators.

Option D: ModelEvaluationOp is used for assessing model quality (calculating metrics like AUC/Recall), not for the action of uploading or deploying a model.