Description: Monitoring Jupyter Notebook refers to the practice of overseeing the performance and resource usage of Jupyter Notebooks during their execution. This practice is essential for data scientists and developers who use Jupyter for data analysis, modeling, and visualization. Through monitoring, bottlenecks in performance can be identified, resource usage optimized, and it can be ensured that notebooks run efficiently. Key features of monitoring include collecting metrics on CPU usage, memory, and execution time, as well as the ability to log and visualize this data in real-time. This allows users to make informed decisions about optimizing their code and managing resources in cloud computing environments. The relevance of monitoring Jupyter Notebook lies in its ability to enhance productivity and efficiency in data analysis projects, where performance can be critical. Furthermore, in contexts involving shared resources, monitoring becomes even more essential, as proper management can lead to more cost-effective and sustainable use of computing infrastructure.
