Model deployment

1. Model deployment is the process of taking a trained machine learning (ML) model and making it available for real-world use in a production environment. 
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3. This typically involves packaging the model, deploying it to a server or platform, and exposing it through an API so that users, applications, or other AI systems can make predictions (or "inferences") on new data. 
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5. Key aspects include preparing the deployment environment, choosing a serving strategy (like real-time APIs or batch processing), and ensuring the model runs reliably and efficiently