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Explain the concept of model parallelism in distributed machine learning. How does it differ from data parallelism and in what scenarios would you prefer to use model parallelism over data parallelism in Spark ML?
A
Model parallelism involves splitting the model across nodes; it is used when the model is too large to fit on a single node.
B
Model parallelism is identical to data parallelism but with models instead of data.
C
Model parallelism is not supported in Spark ML; only data parallelism is used.
D
Model parallelism is less efficient than data parallelism and is rarely used.
