End-to-End ML Pipeline - Interactive Visualization

An ML pipeline chains together all the steps from raw data to a production prediction: data cleaning, feature engineering, scaling, encoding, model training, and evaluation. Using a scikit-learn Pipeline object ensures transformers are fit only on training data - preventing data leakage where test-set information contaminates preprocessing. Reproducibility, reusability, and leakage prevention are the three reasons every production ML system uses pipelines.

• Trace data flow through each stage - raw data, cleaned data, encoded features, scaled features, model predictions
• See data leakage in action: fitting a scaler on the full dataset before splitting leaks test statistics into training
• Understand how sklearn Pipeline prevents leakage: fit() on training data only, transform() applied identically to test
• Learn column transformer: apply different preprocessing to numerical vs categorical vs text features simultaneously
• See how to serialize a trained pipeline for deployment - the entire preprocessing + model stack saved as one artifact

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.