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.

The low training cost of online ML algorithms make them very adaptable—they can react to changes in data in real time. This allows for an immediate response to new information.

Coupled with scale for high-frequency and large streaming data, it’s clear why online learning is preferred in many use cases. For applications like credit card fraud or banking algorithms, where purchases are being made at an incredible rate across the globe, traditional ML that runs in large batches takes time and money to train. By the time it finishes one round of training, there’s tons of new data that make it outdated.

The ...