Online Learning

Online ML is a subset of ML that involves training and updating models in real time as soon as new data becomes available. It allows for the continuous learning and adaptation of models without the need to retrain from scratch. Compared to batch learning (how traditional ML models are trained), online ML algorithms are better for handling streaming data and are robust to dynamic and evolving environments. Naturally, these models have their own risks that need to be accounted for in any ML pipeline involving them.

Online learning advantages

The advantages of online learning are significant because they’re optimized for specific scenarios involving high-frequency data streams.