What is the purpose of distributed machine learning?

Distributed machine learning involves training machine learning models across multiple computers or nodes simultaneously. The primary purpose of this approach is to handle large datasets and complex models that would otherwise be difficult or impossible to process on a single machine. By distributing the workload, resources such as memory and processing power are utilized more effectively, which can lead to faster training times and the ability to tackle larger datasets.

This parallelized training process allows for scaling up machine learning tasks so that as datasets grow or models become more complex, performance doesn't degrade. Thus, it’s particularly advantageous when working with big data scenarios where data can't fit into the memory of a single machine or when computational efforts need to be shared among multiple machines to speed up the training process.

While other options touch on aspects related to data and processing, they do not accurately capture the essence of what distributed machine learning achieves. For example, reducing data input requirements, avoiding preprocessing, or simplifying the overall training process are not primary objectives of distributed machine learning, as this method specifically emphasizes computational distribution rather than altering the foundational requirements of the machine learning processes.