How Is Online Machine Learning Unique From Traditional Machine Learning?

• When data is continuously streamed, online learning is essential in order to do real-time analysis.

Online machine learning (OML) is a type of machine learning (ML) in which data is acquired sequentially and utilised to update the best predictor for future data at each step, in contrast to batch learning techniques, which generate the best predictor by learning on the full training data set at once. In comparison to “conventional” machine learning solutions, online machine learning takes a fundamentally different approach, one that recognises that learning environments can (and frequently do) change from second to second. It is employed in cases when the algorithm must adapt dynamically to new patterns in the data or when the data is generated as a function of time.

OML is a widely used technique in areas of machine learning when training over the complete dataset is computationally impractical, necessitating the employment of out-of-core algorithms. OML, in its simplest form, is a machine learning technique that ingests a sample of real-time data, one observation at a time. OML applies to challenges in which samples are provided over time, and their probability distributions are also expected to change over time. As a result, the model is anticipated to evolve to capture and respond to such changes at a similar rate. This could be viewed as a benefit in a particular industry where real-time personalisation is critical.

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