Saving Models with joblib and pickle
Learners will serialise a trained pipeline with both joblib and pickle, load it back, and verify predictions are identical to confirm successful round-trip.
Why Model Persistence Matters
Training a machine learning model is expensive: it can take minutes to hours and consumes significant compute. Model persistence saves the fitted model to disk so you can reload it instantly for inference without retraining. This is the bridge between the data science notebook and a production system — the serialised model file is the deployable artefact that data engineers package and serve.
What Gets Saved in a Model File?
When you serialise a fitted sklearn model or pipeline, the file captures: all fitted parameters (e.g., scaler mean and variance, tree structure, logistic regression coefficients), hyperparameter settings, and the Python class definition reference. It does NOT include the training data. Loading the file reconstructs a Python object ready to call predict immediately.