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Machine Learning with Spark - Second Edition by Nick Pentreath, Manpreet Singh Ghotra, Rajdeep Dua

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Batch versus real time

In the previous sections, we outlined the common batch processing approach, where the model is retrained using all data or a subset of all data, periodically. As the preceding pipeline takes some time to complete, it might not be possible to use this approach to update models immediately as new data arrives.

While we will be mostly covering batch machine learning approaches in this book, there is a class of machine learning algorithms known as online learning; they update immediately as new data is fed into the model, thus enabling a real-time system. A common example is an online-optimization algorithm for a linear model, such as stochastic gradient descent. We can learn this algorithm using examples. The advantages ...

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