You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performin

Creating an AutoML tabular model involves rebuilding the model from scratch, contradicting the requirement to add functionality to an *existing custom model* with minimal effort.

Creating a BigQuery ML deep neural network model also means rebuilding the model, which is not applicable when the goal is to enhance an existing custom model.

While Vertex AI Model Registry supports configuring feature-based attribution, for a custom model deployed in a *custom serving container*, directly embedding a highly accurate method like sampled Shapley into the container offers finer control and potentially more straightforward integration with existing custom logic for compliance needs.

Updating the custom serving container to include sampled Shapley-based explanations provides maximum control over the explanation generation process, which is crucial for compliance and ensuring high accuracy for a custom model. This method directly embeds the explanation logic, leveraging the existing custom container setup with comparatively minimal effort compared to rebuilding the entire model or deployment.