Overload
In Data Science, overload refers to the situation where a system or a model is given more input or data than it can handle, resulting in a degradation of performance or accuracy. Overload can occur in various stages of the data science pipeline, such as data collection, data preprocessing, feature engineering, model training, and model deployment. Overload can be caused by various factors, such as limited computational resources, insufficient memory, slow I/O operations, network latency, or poor algorithm design. Overload can lead to various problems, such as increased processing time, decreased throughput, increased error rates, decreased model accuracy, or even system crashes. To avoid overload, data scientists need to carefully design their systems and models, optimize their algorithms, use efficient data structures and libraries, monitor their system performance, and scale their systems as needed.
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