You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the…

Training a TensorFlow model on Vertex AI typically requires writing code for model definition, training, and often manual hyperparameter tuning, which directly contradicts the requirement of completing steps "without writing code."

Vertex AutoML for tabular data is specifically designed to handle end-to-end machine learning workflows, including automated exploratory data analysis, feature engineering, model selection, training, hyperparameter tuning, and serving for classification tasks on structured datasets, all with minimal to no code required from the user.

While BigQuery ML allows training models with SQL, it's a low-code approach rather than no-code and does not provide the integrated automated EDA, comprehensive feature selection, and hyperparameter tuning capabilities as a complete managed workflow.

Using scikit-learn in Notebooks with the pandas library necessitates significant coding for all stages of the ML workflow, including data analysis, feature engineering, model training, and tuning, directly violating the 'without writing code' constraint.