Network Congestion
Network congestion occurs when the demand for network resources exceeds the available capacity, resulting in a degradation of network performance. In data science and artificial intelligence, network congestion can have a significant impact on the performance of distributed systems, such as those used for machine learning training or real-time data processing. Congestion can lead to increased latency, packet loss, and reduced throughput, which can in turn affect the accuracy and reliability of machine learning models. To mitigate the effects of network congestion, various techniques can be employed, such as traffic shaping, load balancing, and congestion control algorithms.
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