Online Inference

A real-time prediction mechanism that processes individual requests for predictions with minimal latency. This approach is characterized by its ability to deliver immediate responses, making it suitable for applications requiring instantaneous decision-making, such as recommendation systems or fraud detection. Mathematically, online inference can be expressed as Y = f(x), where Y is the predicted output for a single input x, processed through the model function f. Key algorithms employed in online inference include logistic regression, support vector machines, and neural networks optimized for low-latency environments. The architecture typically involves deploying models in a microservices framework, allowing for scalable and responsive systems that can handle varying loads. Online inference contrasts with batch inference, where predictions are made on aggregated data, emphasizing the need for efficient resource management and low response times in real-time applications.