Parallel and distributed processing refers to the ability to distribute and process large data sets in parallel across multiple nodes or hardware devices to speed up processing time and improve performance. Instead of processing data on a single device, parallel and distributed processing divides data into several parts and processes them simultaneously on different devices.
In the context of artificial intelligence and machine learning, parallel and distributed processing is used to train larger and more complex models on large data sets. This is achieved by using techniques such as cluster processing and GPU processing to partition and process data in parallel across multiple devices.
Parallel and distributed processing is also used in other fields of computing, such as scientific data processing, simulation of complex systems, and real-time processing of large datasets in the cloud. The ability to process large amounts of data in parallel and distributed processing is fundamental to the success of many computing projects and has been a key factor in the advancement of technology in recent decades.
Typically, Machine Learning is used to solve business problems in various sectors and areas where different algorithms are applied.
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