Distributed Processing
Distributed processing is a method of dividing a large task into smaller sub-tasks and distributing them across multiple computers or nodes in a network. Each node processes its assigned sub-task independently and communicates with other nodes to exchange data and coordinate the overall task. Distributed processing is commonly used in data science and artificial intelligence applications to handle large datasets and complex computations. By distributing the workload, it can significantly reduce the time required to complete a task and improve overall performance and scalability. Additionally, distributed processing can provide fault tolerance and high availability by allowing the system to continue functioning even if one or more nodes fail.
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