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Answer: Leverage data parallelism and model parallelism, and optimize resource allocation.
Leveraging both data parallelism and model parallelism, along with optimizing resource allocation, ensures efficient and effective model training in a distributed environment. Ignoring data parallelism or training on a single node can lead to inefficient use of computational resources and suboptimal model performance.
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In a distributed computing environment, describe the challenges you might face when training a machine learning model using Spark ML and how you would address these challenges to ensure efficient and effective model training. Discuss the considerations for data parallelism, model parallelism, and resource allocation.
A
Ignore data parallelism and use only model parallelism.
B
Leverage data parallelism and model parallelism, and optimize resource allocation.
C
Train the model on a single node without considering distributed computing.
D
Use only the most recent data for model training.
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