Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects simply to a non-technical, business audience. Over…
Online machine learning is a type of machine learning (ML) that continuously improves performance by processing new input in real or near-real time.
Online learning algorithms are often compared to batch learning algorithms. Batch learning algorithms are static — they use large data sets to train and validate outcomes before the model moves to production. In contrast, online learning algorithms are trained incrementally as new data becomes available.
It is called online machine learning because the ML programming must be connected to a network that provides a dynamic input stream.
Traditionally, many ML programs took a fixed set of input data from a file, and then worked on it sequentially. It is easy to think of basic machine learning programs that use fixed data sets such as the contents of database tables.
With online machine learning, it is a little different. The machine learning program may be taking in real-time information from sensors in a manufacturing environment, or text input from users over the internet, or something else that comes in as input in real time. The machine learning program is made to deal with these real-time data streams and produce results. Online machine learning can help make machine learning algorithms more capable in many fields and industries.
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Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.
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