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Answer: Model parallelism involves splitting the model across nodes; it is used when the model is too large to fit on a single node.
Model parallelism involves splitting the model itself across multiple nodes, allowing parts of the model to be trained concurrently. This approach is particularly useful when dealing with very large models that cannot fit into the memory of a single node. Unlike data parallelism, where the entire model is replicated across nodes and different subsets of the data are used for training, model parallelism focuses on distributing different parts of the model to different nodes, which can be more efficient for certain types of large-scale models.
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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.
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