Model Persistence
Explore how to persist machine learning models by saving and loading them with the Joblib library in scikit-learn. Understand the benefits of model persistence such as avoiding retraining, enabling reproducible results, and facilitating deployment in production environments. This lesson guides you through exporting a trained model and reusing it efficiently on new data.
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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
Exporting and importing models provide several advantages: ...