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AI for IT Operations (AIOps) is a powerful platform that Site ReliabilityEngineers (SREs) use to automate and streamline operational workflows withminimal human intervention. Automated log analysis is a critical task in AIOpsas it provides key insights for SREs to identify and address ongoing faults.Tasks such as log format detection, log classification, and log parsing are keycomponents of automated log analysis. Most of these tasks require supervisedlearning; however, there are multiple challenges due to limited labelled logdata and the diverse nature of log data. Large Language Models (LLMs) such asBERT and GPT3 are trained using self-supervision on a vast amount of unlabeleddata. These models provide generalized representations that can be effectivelyused for various downstream tasks with limited labelled data. Motivated by thesuccess of LLMs in specific domains like science and biology, this paperintroduces a LLM for log data which is trained on public and proprietary logdata. The results of our experiments demonstrate that the proposed LLMoutperforms existing models on multiple downstream tasks. In summary, AIOpspowered by LLMs offers an efficient and effective solution for automating loganalysis tasks and enabling SREs to focus on higher-level tasks. Our proposedLLM, trained on public and proprietary log data, offers superior performance onmultiple downstream tasks, making it a valuable addition to the AIOps platform.

Learning Representations on Logs for AIOps