Empowering AIOps: Leveraging Large Language Models for IT Operations Management

• URL: http://arxiv.org/abs/2501.12461v2
• Date: Thu, 23 Jan 2025 16:31:51 GMT
• Title: Empowering AIOps: Leveraging Large Language Models for IT Operations Management
• Authors: Arthur Vitui, Tse-Hsun Chen,
• Abstract summary: We aim to integrate traditional predictive machine learning models with generative AI technologies like Large Language Models (LLMs) LLMs enable organizations to process and analyze vast amounts of unstructured data, such as system logs, incident reports, and technical documentation. We propose innovative methods to tackle persistent challenges in AIOps and enhance the capabilities of IT operations management.
• Score: 0.6752538702870792
• License:
• Abstract: The integration of Artificial Intelligence (AI) into IT Operations Management (ITOM), commonly referred to as AIOps, offers substantial potential for automating workflows, enhancing efficiency, and supporting informed decision-making. However, implementing AI within IT operations is not without its challenges, including issues related to data quality, the complexity of IT environments, and skill gaps within teams. The advent of Large Language Models (LLMs) presents an opportunity to address some of these challenges, particularly through their advanced natural language understanding capabilities. These features enable organizations to process and analyze vast amounts of unstructured data, such as system logs, incident reports, and technical documentation. This ability aligns with the motivation behind our research, where we aim to integrate traditional predictive machine learning models with generative AI technologies like LLMs. By combining these approaches, we propose innovative methods to tackle persistent challenges in AIOps and enhance the capabilities of IT operations management.

Related papers

• Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications [17.624263707781655]
  Artificial intelligence (AI), machine learning, and deep learning have become transformative forces in big data analytics and management. This article delves into the foundational concepts and cutting-edge developments in these fields. By bridging theoretical underpinnings with actionable strategies, it showcases the potential of AI and LLMs to revolutionize big data management.
  arXiv  Detail & Related papers  (2024-10-02T06:24:51Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Are Large Language Models the New Interface for Data Pipelines? [3.5021689991926377]
  A Language Model is a term that encompasses various types of models designed to understand and generate human communication. Large Language Models (LLMs) have gained significant attention due to their ability to process text with human-like fluency and coherence.
  arXiv  Detail & Related papers  (2024-06-06T08:10:32Z)
• The Foundations of Computational Management: A Systematic Approach to Task Automation for the Integration of Artificial Intelligence into Existing Workflows [55.2480439325792]
  This article introduces Computational Management, a systematic approach to task automation. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow.
  arXiv  Detail & Related papers  (2024-02-07T01:45:14Z)
• AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges [60.56413461109281]
  Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes. We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful. We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
  arXiv  Detail & Related papers  (2023-04-10T15:38:12Z)
• OpenAGI: When LLM Meets Domain Experts [51.86179657467822]
  Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents. We introduce OpenAGI, an open-source platform designed for solving multi-step, real-world tasks.
  arXiv  Detail & Related papers  (2023-04-10T03:55:35Z)
• Designing an AI-Driven Talent Intelligence Solution: Exploring Big Data to extend the TOE Framework [0.0]
  This study aims to identify the new requirements for developing AI-oriented artifacts to address talent management issues. A design science method is adopted for conducting the experimental study with structured machine learning techniques.
  arXiv  Detail & Related papers  (2022-07-25T10:42:50Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks [4.04540578484476]
  Intelligence Task Ontology and Knowledge Graph (ITO) is a comprehensive resource on artificial intelligence tasks, benchmark results and performance metrics. ITO is a richly structured and manually curated resource on artificial intelligence tasks, benchmark results and performance metrics. The goal of ITO is to enable precise and network-based analyses of the global landscape of AI tasks and capabilities.
  arXiv  Detail & Related papers  (2021-10-04T13:25:53Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.