AI model deployment is the process of integrating trained machine learning models into production environments where they can serve predictions to end users, applications, or other systems. This critical phase bridges the gap between experimental modeling and real-world business value, transforming notebooks and research artifacts into scalable, reliable inference services. Whether deploying via REST APIs, edge devices, or cloud platforms, successful deployment requires careful orchestration of model packaging, serving infrastructure, monitoring, and operational workflows. Modern deployment practices emphasize automation, observability, and resilience—treating models as first-class software artifacts that evolve through continuous integration and delivery pipelines.