12. Inference & Deployment Checklist

Once the final model is trained and evaluated:

1. Retrain on full data (train + validation) before deployment if you used a hold-out val set.
2. Save the fitted pipeline (not just the model — you need the scaler/imputer too).

   import joblib
   joblib.dump(pipe, 'model_pipeline.pkl')
   loaded_pipe = joblib.load('model_pipeline.pkl')

3. Prediction on new data:

   y_pred = loaded_pipe.predict(X_new)
   y_proba = loaded_pipe.predict_proba(X_new)  # for probability scores

4. Check for distribution shift — monitor whether real-world input features start drifting from the training distribution.
5. Calibration — if probabilities matter (not just class labels), check if predict_proba is well-calibrated using a calibration curve. Use CalibratedClassifierCV if needed.

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