How can you optimize the performance of a machine learning model that processes a large dataset?
- By parallelizing training across multiple GPUs or distributed computing systems.
- By reducing the model's capacity to handle large datasets.
- By training the model on a single machine with maximum resources.
- Large datasets cannot be processed efficiently in machine learning.
To optimize the performance of a model on large datasets, you can use techniques like data parallelism and distributed computing. This involves training the model on multiple GPUs or across multiple machines to speed up training and handle the large dataset efficiently. Training on a single machine may not be feasible due to memory and processing limitations. Reducing model capacity is not a recommended approach.
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