A model update refreshes a machine learning system with new data or improved algorithms, boosting accuracy and relevance. Teams apply it through retraining or fine-tuning, often via automated pipelines. Businesses, developers, and end users all gain from smarter predictions, reduced bias, and better performance over time.
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