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Iteration is a basic building block for a ML library. It is required for training ML models for both offline and online Training. In general, two types of iterations is required:

1. Bounded Iteration: Used Usually used in the offline case. In this case the algorithm usually train on a bounded dataset, it updates the parameters for multiple rounds until convergence.
2. Unbounded Iteration: Used Usually used in the online case, in this case the algorithm usually train on an unbounded dataset. It accumulates a mini-batch of data and then do one update to the parameters. 

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1. The dataset is unbounded. The Train operator could not cache all the data in the first round.
2. The training algorithm might change be changed to others like FTRL. But we keep using SGD in this example since it does not affect showing the usage of the iteration.

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