In artificial intelligence and machine learning, the term “online” refers to processes or systems that operate in real-time or incrementally as new data becomes available, rather than waiting for a complete dataset before processing. This contrasts with “offline” approaches, where models are typically trained or updated in batch mode using historical data. Online methods are especially important in dynamic environments where data flows continuously, such as financial markets, recommendation systems, real-time language translation, fraud detection, and interactive AI applications.
Online learning (sometimes called incremental learning) is a classic example. In online learning, the model is updated sequentially, one data point or a small batch at a time. This allows the system to adapt quickly to changes or new patterns in the data. For instance, a spam filter that updates itself with each new email is using an online approach. This is in contrast to retraining the filter from scratch on a large, static dataset, which would be an offline approach.
Online inference is another use of the term. Here, an AI model makes predictions or decisions on new data as soon as it’s received. For example, a chatbot responding to user queries or a recommendation engine suggesting products in real-time both rely on online inference. The ability to process and respond to data instantly is critical in many real-world AI applications where delays can reduce the value of the system.
Online [training](https://thealgorithmdaily.com/online-training) and online models are also common phrases. Online [training](https://thealgorithmdaily.com/online-training) is the process of updating a model as new data arrives, while an online model is one that is designed to be updated, queried, or deployed in real-time. These systems often need to be robust to incomplete or noisy data, and they must handle the challenges of concept drift (when the underlying patterns in the data change over time).
A key advantage of online approaches is their scalability and flexibility. Systems don’t need to load or process the entire dataset at once, which saves on memory and computation. This is why online algorithms are often chosen for big data or streaming data scenarios. However, online systems also require careful design to avoid issues like catastrophic forgetting (where the model forgets previously learned information as new data arrives).
It’s worth noting that “online” can also refer more generally to any AI system or process that interacts with the world in real-time, including online services, real-time analytics, and interactive agents. In all these cases, the core idea is the ability to process, learn from, or respond to data as it happens, rather than after the fact.
