Model serving is the process of deploying and making a trained AI or machine learning model available for real-time or batch predictions in a production environment. Once a model has been trained, it needs to be efficiently served so applications, APIs, or users can access it to make inferences on new data without retraining.

One common use case for model serving is real-time recommendation systems, where AI models analyze user behavior and instantly suggest content, products, or services. In industries like finance and healthcare, model serving enables fraud detection, medical image analysis, and predictive analytics, ensuring AI-driven insights are delivered quickly and efficiently.

Effective model serving requires low-latency responses, scalability, and efficient resource management, which is why organizations leverage cloud-based model serving solutions, containerized deployments (e.g., Docker, Kubernetes), and specialized inference engines (e.g., TensorFlow Serving, TorchServe) to optimize performance. As AI adoption grows, scalable and efficient model serving is essential for delivering seamless and intelligent experiences in real-world applications.