Production ML pipeline implements stratified 5-fold cross-validation on a 560-sample dataset, splitting into 80% training and 20% testing sets. Preprocessing applies exclusively to training data before feeding multimodal temporal inputs into a deep learning model with early stopping to prevent overfitting. Evaluation metrics compare the deep learning model against baseline models running in parallel, measuring both accuracy and real-time inference latency. This architecture demonstrates best practices for preventing data leakage, ensuring fair model comparison, and validating production readiness. Fork this diagram on Diagrams.so to customize preprocessing steps, adjust cross-validation folds, or add hyperparameter tuning stages for your ML workflow.