AI for IT Operations (AIOps) is a powerful platform for Site Reliability Engineers to automate and streamline operational workflows. Automated log analysis, a critical task in AIOps, provides key insights to identify and address faults. Logs can capture a variety of information on an application, giving a deeper view of potential issues and helping to diagnose an ongoing problem. Tasks like format detection, classification, parsing, anomaly detection, and summarization are the key components of automated log analysis. These tasks require supervised learning with massive labeled data; however, there are multiple challenges due to the limited labeled and diverse nature of log data. Large Language Models (LLMs) like BERT and GPT3 are trained using self-supervision on unlabeled data. These models provide generalized representations that can be effectively used for various downstream tasks with limited labeled data. This demo will showcase LLM for log data, BERTOps - a model for AIOps that uses the IBM Slate model as a base. Our experiments demonstrate that BERTOps, when fine-tuned using a limited amount of labeled data (few-shot setting) tailored to each specific AIOps downstream task, surpasses the performance of state-of-the-art transformer models. This underscores its significance as a cost-effective and valuable augmentation to the AIOps platform. We will also show a demo and an interactive user interface that provides a summarized view of the log data and the detected anomalous log windows to help diagnose a fault. The demo uses a framework incorporating the various fine-tuned models on BERTOps. We will also demonstrate why this framework is useful when domain experts are required for log diagnosis in a complex industrial application setting while significantly reducing manual effort and visual overload. The demo will highlight specific use cases and applications of the framework in IBM Software Support, IBM Automation and IBM Consulting.