As large language models (LLMs) grow increasingly sophisticated and pervasive, their application to various Artificial Intelligence for IT Operations (AIOps) tasks has garnered significant attention. However, a comprehensive understanding of the impact, potential, and limitations of LLMs in AIOps remains in its infancy. To address this gap, we conducted a detailed survey of LLM4AIOps, focusing on how LLMs can optimize processes and improve outcomes in this domain. We analyzed 183 research papers published between January 2020 and December 2024 to answer four key research questions (RQs). In RQ1, we examine the diverse failure data sources utilized, including advanced LLM-based processing techniques for legacy data and the incorporation of new data sources enabled by LLMs. RQ2 explores the evolution of AIOps tasks, highlighting the emergence of novel tasks and the publication trends across these tasks. RQ3 investigates the various LLM-based methods applied to address AIOps challenges. Finally, RQ4 reviews evaluation methodologies tailored to assess LLM-integrated AIOps approaches. Based on our findings, we discuss the state-of-the-art advancements and trends, identify gaps in existing research, and propose promising directions for future exploration.

Telecommunication Networks (TNs) have become the most important infrastructure for data communications over the last century. Operations and maintenance (O&M) is extremely important to ensure the availability, effectiveness, and efficiency of TN communications. Different from the popular O&M technique for IT systems (e.g., the cloud), artificial intelligence for IT Operations (AIOps), O&M for TNs meets the following three fundamental challenges: topological dependence of network components, highly heterogeneous software, and restricted failure data. This article presents TelOps, the first AI-driven O&M framework for TNs, systematically enhanced with mechanism, data, and empirical knowledge. We provide a comprehensive comparison between TelOps and AIOps, and conduct a proof-of-concept case study on a typical O&M task (failure diagnosis) for a real industrial TN. As the first systematic AI-driven O&M framework for TNs, TelOps opens a new door to applying AI techniques to TN automation.

With the rise of artificial intelligence, machine learning and big data, organizations have become increasingly aware of the importance of MLOps (Machine Learning Operations), ModelOps, DataOps, and AIOps. Through this blog post, we will discuss the differences between these various approaches in order to better understand their individual roles within an organization. We then explore how Machine Learning, Model Management and Data Infrastructure intersect in MLOps. Finally, we discuss both the benefits and challenges when it comes to implementing these operations systems. MLOps, ModelOps, DataOps and AIOps are rapidly growing in importance as organizations look to leverage the power of artificial intelligence, machine learning and big data.

The 48th IT Press Tour had the opportunity to meet with the management team at CloudFabrix. This is the team's fourth startup, with the previous three being sold to Cisco. They are doing a lot of interesting things automating operations. Multi-cloud challenges continue, as 73% of enterprises use two or more clouds. This is projected to be 81% by 2024.

The artificial intelligence for IT operations (AIOps) platform market is moving faster than can really be imagined, both in terms of vendors as well as the capabilities that make up AIOps solutions. This was driven by a continued increase of unmanageable data volumes and continued desire for business insights. To address these changes since the last Forrester Wave on AIOps two years ago, Forrester published the AIOps Reference Architecture, which defines the 18 functions required to deliver AIOps solutions. Additionally, we published guidance on the different perspectives of how to approach AIOps to address a growing organizational need to help put AIOps into practice. The Forrester Wave: Artificial Intelligence For IT Operations, Q4 2022 is focused on technology-centric AIOps solutions, which was a key element of the entry criteria in addition to single-code base and/or UI, stand-alone product, and domain-agnostic interoperability. Operationally aligned and process-centric AIOps vendors were not included in this research but will be highlighted in future Forrester research efforts.

For greater efficiency and innovation, IT leaders are exploring AI applications that streamline operations, generate text and images, and even write code. With more streamlined operations and greater agility you can embrace emerging trends in AI more readily. Corporate operations switch to a backup datacenter after a local power outage darkens the primary systems. As such scenarios unfold, virtual assistants text alerts and summaries about these events and resolutions to their human counterparts. Until recently, IT leaders have dreamt of such autonomous computing remediation for decades.

The launch of ChatGPT and GPT 3.5 (Generative Progressive Transformer-3.5) -- which many claim will herald a new era in dialogue-based conversational AI -- has ended the year on a high for conversational AI. People are using ChatGPT for tasks ranging from correcting code errors to rewriting the Bohemian Rhapsody and the number of ChatGPT users surpassed the million mark in less than a week last month. While 2022 was about newer and more advanced tools and models, commercial use cases, regulation, and standardisation of AI are expected to define 2023 for this domain. Here's what to expect from the AI industry in 2023. Generative AI, which is artificial intelligence that can create text, images, videos etc. without supervision, set the tone for this year and the trend will spill on to 2023 as well.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. AIOps is an emerging IT practice of applying analytics and machine learning to IT operations that enables reduced MTTR [Mean Time To Respond], predictive analysis, proactive performance monitoring, and provides actionable insights for faster decision-making.

Welcome to insideBIGDATA's "Heard on the Street" round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace. We invite submissions with a focus on our favored technology topics areas: big data, data science, machine learning, AI and deep learning. Data is the new oil.

When legendary computer scientist Jim Gray accepted the Turing Award in 1999, he laid out a dozen long-range information technology research goals. One of those goals called for the creation of trouble-free server systems or, in Gray's words, to "build a system used by millions of people each day and yet administered and managed by a single part-time person." Gray envisioned a self-organizing "server in the sky" that would store massive amounts of data, and refresh or download data as needed. Today, with the emergence and rapid advancement of artificial intelligence (AI), machine learning (ML) and cloud computing, and Microsoft's development of Cloud Intelligence/AIOps, we are closer than we have ever been to realizing that vision--and moving beyond it. Over the past fifteen years, the most significant paradigm shift in the computing industry has been the migration to cloud computing, which has created unprecedented digital transformation opportunities and benefits for business, society, and human life.