Monitoring Evaluation for Large-Scale Environments and Optimizing Data System Health

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In today's large-scale environments, keeping systems healthy and performing at their best is crucial. This work presents a practical approach to system monitoring that combines various data sources and methods. It explores how service logs and Prometheus metrics provide detailed insights into operational issues and their effects on system performance while also enabling real-time monitoring and alerts via email. The project uses model training techniques to spot problems before affecting system health. Using it, organizations can proactively identify and address anomalies by applying data-driven and unsupervised learning methods, leading to better system performance. We also integrate Kubeflow, an open-source platform for managing machine learning workflows, simplifying the deployment of these models. This combined approach provides a framework for monitoring and optimizing large-scale environments, ensuring data systems remain resilient and effective as operational demands evolve..

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