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Answer: To enable parallel processing, thereby increasing the speed of training models and handling large datasets efficiently
**Correct Option: D. To enable parallel processing, thereby increasing the speed of training models and handling large datasets efficiently** **Explanation:** Distributed computing is crucial in machine learning for handling large-scale data and complex models by dividing tasks across multiple processors or machines. The primary benefits include: - **Accelerated training:** Parallel processing reduces the time required to train models on large datasets. - **Scalability:** It allows the system to handle growing data sizes and model complexities by adding more resources. - **Efficiency:** Enables the processing of datasets that are impractical for a single machine due to size or computational demands. **Why other options are not the primary purpose:** - **A. To simplify the codebase and reduce development time:** Distributed computing can introduce complexity in code management and coordination across machines. - **B. To inherently improve the predictive accuracy of machine learning models:** Accuracy is determined by the model and data, not the computing method. - **C. To significantly lower the operational costs by minimizing resource usage:** While cost efficiency can be a benefit, the main goal is to enhance performance and scalability to meet project demands.
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In the context of machine learning projects that involve large datasets and complex models, a team is considering the adoption of distributed computing to optimize their workflow. The project has strict deadlines and requires the processing of datasets that are too large for a single machine. Additionally, the team is looking for a solution that not only accelerates the training process but also scales efficiently with increasing data sizes. Given these constraints, which of the following is the main advantage of employing distributed computing in this scenario? (Choose one correct option)
A
To simplify the codebase and reduce development time
B
To inherently improve the predictive accuracy of machine learning models
C
To significantly lower the operational costs by minimizing resource usage
D
To enable parallel processing, thereby increasing the speed of training models and handling large datasets efficiently
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