Online Learning
Last Updated: July 29, 2026 | By Mihail Sebastian | AI Dictionary
A machine learning approach where the model updates incrementally as each new data point arrives, instead of training once on a full dataset.
What is Online Learning?
Online learning is a machine learning approach in which the model updates incrementally as each new data point or small batch arrives, instead of being trained once on a complete dataset.
The term has nothing to do with taking courses over the internet. The contrast is with batch learning, where a model trains on a fixed dataset and stays frozen until someone retrains it.
How Online Learning Works
Each incoming example triggers a small update to the model’s parameters and is then discarded. Memory stays low because the full history is never stored, and the model tracks changes in the data as they happen.
That tracking is the main reason to choose it. When the input distribution shifts – data drift – a batch-trained model quietly degrades until the next retrain, while an online model adapts continuously.
The price is fragility. A burst of bad or malicious data updates the model just as readily as good data, so production systems pair online learning with monitoring and rollback safeguards.
Example of Online Learning
A navigation app predicts travel times from live traffic. Road conditions change by the minute (an accident, a closed lane, rush hour starting early), so a model trained last night is already stale by the morning commute.
An online model ingests each new batch of GPS speed reports and nudges its estimates immediately: minutes after the accident, predicted times through that stretch rise. When the road clears, the next reports pull them back down. No retraining pipeline runs; the model simply never stops learning.
Related AI terms: Machine Learning · Data Drift · Model Training · Stochastic Gradient Descent
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Mihail Sebastian — Writes about AI governance, regulation, and the technology behind them. Placeholder bio — replace with a real credential line. About