Discuss the role of data parallelism in distributed machine learning and how Spark ML leverages this concept to improve training efficiency. Provide an example of a machine learning algorithm where data parallelism is particularly effective.

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Data parallelism involves replicating the entire dataset across nodes; Spark ML uses this for all algorithms.

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Data parallelism splits the dataset across nodes, each training on a subset; Spark ML uses this in gradient-based algorithms like logistic regression.

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Data parallelism is not used in Spark ML; model parallelism is more effective.

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Data parallelism is only effective in small-scale datasets; Spark ML uses other methods for large datasets.

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Discuss the role of data parallelism in distributed machine learning and how Spark ML leverages this concept to improve training efficiency. Provide an example of a machine learning algorithm where data parallelism is particularly effective.