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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.