In machine learning, model support refers to the ongoing maintenance, monitoring, and updates required to keep an AI system accurate and reliable after deployment. It involves retraining with new data, debugging performance drift, and ensuring scalability. Data scientists and ML engineers benefit most, as it prevents model decay and preserves business value over time.
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