Distributed Machine Learning


Distributed Machine Learning is the process of training machine learning models using multiple computing resources across a network. It involves breaking down large datasets into smaller subsets and distributing them across multiple machines for parallel processing. The results are then combined to create a final model. Distributed Machine Learning is used to handle large datasets that cannot be processed on a single machine due to memory or processing power limitations. It also enables faster training times and improved model accuracy. Distributed Machine Learning can be implemented using various frameworks such as Apache Spark, TensorFlow, and PyTorch.


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