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Democratizing Performance Monitoring with AI

EasyChair Preprint no. 12912

14 pagesDate: April 5, 2024


Performance monitoring is a critical aspect of managing and optimizing various systems and processes across different domains. Traditionally, performance monitoring has been a complex and resource-intensive task, often limited to expert personnel due to the technical expertise required. However, recent advancements in artificial intelligence (AI) have opened up new possibilities for democratizing performance monitoring and making it accessible to a wider audience.

This abstract presents an overview of the concept of democratizing performance monitoring with AI. It explores how AI technologies can be leveraged to simplify and automate performance monitoring processes, enabling non-experts to gain valuable insights and make data-driven decisions.

The democratization of performance monitoring involves utilizing AI algorithms and techniques to develop intelligent systems that can autonomously collect, analyze, and interpret performance data. These systems can provide real-time monitoring, anomaly detection, root cause analysis, and predictive analytics, among other functionalities. By automating these tasks, AI-based performance monitoring solutions can alleviate the burden on human operators and enable them to focus on higher-level tasks.

Furthermore, the abstract discusses the potential benefits and challenges associated with democratizing performance monitoring with AI. On the one hand, democratization can lead to increased efficiency, scalability, and cost-effectiveness, as AI systems can handle large volumes of data and perform complex analyses at a faster pace. On the other hand, challenges such as data quality, interpretability, and trustworthiness need to be addressed to ensure the reliability and accuracy of AI-driven performance monitoring systems.

Keyphrases: Artificial, monitoring, Technology

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Favour Olaoye and Kaledio Potter},
  title = {Democratizing Performance Monitoring with AI},
  howpublished = {EasyChair Preprint no. 12912},

  year = {EasyChair, 2024}}
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