Answer-first summary for fast verification
Answer: Data parallelism splits the dataset across nodes, each training on a subset; Spark ML uses this in gradient-based algorithms like logistic regression.
Data parallelism in machine learning involves distributing the dataset across multiple nodes, where each node trains a model on its subset of the data. This approach is particularly effective in algorithms that use gradient-based optimization, such as logistic regression, where gradients can be computed independently on each node and then aggregated to update the model. Spark ML leverages data parallelism by distributing data using RDDs or DataFrames and parallelizing computations across the cluster.
Author: LeetQuiz Editorial Team
Ultimate access to all questions.
No comments yet.
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.
A
Data parallelism involves replicating the entire dataset across nodes; Spark ML uses this for all algorithms.
B
Data parallelism splits the dataset across nodes, each training on a subset; Spark ML uses this in gradient-based algorithms like logistic regression.
C
Data parallelism is not used in Spark ML; model parallelism is more effective.
D
Data parallelism is only effective in small-scale datasets; Spark ML uses other methods for large datasets.