Online machine learning

Online machine learning refers to a subset of machine learning methods designed to process and update models incrementally in real-time as new data becomes available. Unlike traditional batch learning, where models are trained on a fixed dataset, online learning continuously adjusts and refines the model with each new data point. This approach is particularly useful for applications where data arrives sequentially and the environment is dynamic, such as real-time analytics, adaptive systems, and streaming data scenarios. By enabling models to evolve and adapt on the fly, online machine learning enhances the ability to make timely predictions and decisions based on the most current information, thereby improving the responsiveness and accuracy of the system.