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You are an ML engineer at a travel company. You have been researching customers’ travel behavior for many years, and you have successfully deployed multiple models that predict customers’ vacation patterns based on historical data. Through your research, you have observed that customers' vacation destinations change based on seasonality and holidays, yet these variations show consistent patterns across different years. To better analyze and compare the performance of your predictive models across different seasons and years, you need a solution that allows you to quickly and easily store and compare the model versions and their performance statistics. What approach should you take?
A
Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.
B
Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.
C
Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.
D
Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.