Concept
Distributed Machine Learning involves partitioning large datasets and computational tasks across multiple nodes to improve efficiency and scalability in model training. This approach leverages parallel processing and data distribution to handle the increasing complexity and size of modern datasets, enabling faster training times and the ability to work with larger models.
Relevant Fields:
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