Saving, Loading, and Using Models in Real Projects
From Training to Deployment
Why save models
After training, you usually want to reuse the model later without retraining from scratch. Saving a model lets you load it in another program, another notebook, or a production system.
Inference in practice
Inference means feeding new input into a trained model and using the output prediction. In a real project, this may happen in a web app, mobile app, or backend service.
Simple deployment flow
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1
Train the model on clean data.
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Evaluate it on a held-out test set.
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3
Save the trained model.
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Load the model in a new environment.
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Use it to make predictions on new inputs.
Training vs Inference
Training changes the model; inference uses the model without changing its learned parameters.
What does inference mean?
Inference is prediction time.
Correct answer: Using a trained model to make predictions
Why save a model after training?
Saved models make deployment and reuse possible.
Correct answer: So it can be reused later without retraining.
During inference, what usually happens to the model's learned parameters?
Inference uses the trained parameters as-is.
Correct answer: They are used without being updated