Model Persistence

Model persistence refers to the ability to save trained ML models to disk and reload them later for reuse. It allows us to store the model’s learned parameters, trained weights, and other necessary information in order to use it again without retraining.

Model persistence is crucial in various scenarios, such as deploying a trained model in a production environmentA live and operational computing environment where software applications and systems are actively deployed and used to serve end-users or customers., sharing models with others, or simply saving models for future use.

Exporting and importing models provide several advantages: ...