Model Deployment Overview Model deployment is the process of taking a trained machine learning model and making it available for production use through APIs, web services, or batch processing systems. When to Use - When productionizing trained models for real-world inference and predictions - When building REST APIs or web services for model serving - When scaling predictions to serve multiple users or applications - When deploying models to cloud platforms, edge devices, or containers - When implementing CI/CD pipelines for ML model updates - When creating batch processing systems for large-…