About This Architecture

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.

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How do I design an ML pipeline with proper cross-validation and prevent data leakage during preprocessing?

This diagram shows a production ML pipeline using stratified 5-fold CV with 80/20 train-test split, applying preprocessing exclusively to training data to prevent leakage. The architecture includes early stopping, parallel baseline comparison, and real-time inference latency metrics for production validation.