Performance tracking is the systematic process of monitoring and evaluating the effectiveness of a machine learning model throughout its lifecycle, from training to deployment. This practice helps identify how well a model is performing against predefined metrics, allowing for timely adjustments and improvements. By keeping tabs on various performance indicators, developers can ensure that their models maintain accuracy and relevance in real-world applications.
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