AI in Production refers to the operational deployment, scaling, and management of machine learning models beyond experimental environments. Unlike traditional software, production ML systems face unique challenges including model drift, data distribution shifts, and performance degradation over time — requiring continuous monitoring, automated retraining, and sophisticated deployment strategies. The field encompasses infrastructure optimization, observability tooling, and governance frameworks that ensure models deliver reliable, cost-effective predictions at scale while maintaining fairness, explainability, and compliance.

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