👉 Feed computing is a type of machine learning paradigm where the model's parameters are updated based on a continuous stream of input data, rather than being trained on a fixed dataset. In this approach, the model processes data in small batches or even individual samples sequentially, making it suitable for real-time applications and scenarios where data arrives continuously. Unlike traditional batch learning, feed computing allows the model to adapt dynamically to new information, improving its performance over time. This is particularly useful in domains like online recommendation systems, real-time fraud detection, and adaptive control systems, where the model needs to learn and adjust rapidly as new data becomes available.
