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As an analyst at a large banking firm developing a robust, scalable ML pipeline to train several regression and classification models with a primary focus on model interpretability and rapid productionization, what should you do?
A
Use Tabular Workflow for Wide & Deep through Vertex AI Pipelines to jointly train wide linear models and deep neural networks
B
Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models
C
Use Tabular Workflow for TabNet through Vertex AI Pipelines to train attention-based models
D
Use Cloud Composer to build the training pipelines for custom deep learning-based models
