Model Persistence
Model persistence refers to the process of saving a trained machine learning model to a file and later loading it back into memory for making new predictions without retraining. This involves serializing the model's learned parameters and structure into a format that can be stored and later deserialized. It's a fundamental step for deploying any machine learning model outside of its training environment.
It's like baking a cake and then putting it into a container to be stored and served later, instead of having to bake a new cake every time someone wants a slice.
Without model persistence, every prediction would require retraining the entire model, making real-world AI applications impractical and inefficient.
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