AB-731: AI Transformation LeaderDemo

In the standard machine learning model development lifecycle, data preparation always precedes training, hyperparameter tuning, evaluation, and deployment. Even when data is consistent and uniform, it must first be gathered, formatted, and prepared (e.g., split into training/validation sets, cleaned, transformed) before it can be fed into a training algorithm. Only after the data is prepared can the model be trained, its hyperparameters tuned, its performance evaluated, and finally deployed for use.