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Answer: Spark ML utilizes a centralized parameter server to distribute updates to all nodes.
In a distributed machine learning setup, maintaining synchronized model parameters across all nodes is crucial for convergence and accuracy. Spark ML employs a centralized parameter server architecture where updates to model parameters are aggregated and then distributed to all worker nodes. This ensures that each node has the most recent updates, facilitating consistent and effective model training across the cluster.
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Discuss the implications of model synchronization in a distributed machine learning environment. How does Spark ML ensure that all nodes have the most up-to-date model parameters during training?
A
Spark ML uses a master-slave architecture where the master node updates all slave nodes.
B
Spark ML employs a peer-to-peer synchronization method for model parameters.
C
Spark ML utilizes a centralized parameter server to distribute updates to all nodes.
D
Spark ML does not support model synchronization; each node trains independently.
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