That’s why we have AIOps to enable automation and enhance IT operations to produce better business outcomes. Now, imagine if we try to discern critical events from logs that originate from a multitude of sources. It’s hard to discern the structure of a log file even if it comes from a single source. If we analyze the contents and structure of these logs, we’ll find very little uniformity. These log files contain records of different activities happening within the system. Log analysis is the task of interpreting what is happening in this digital environment.
In a digital environment, various servers and applications generate massive volumes of logs. You can create an account here to follow along. But first, let’s explore the ins and outs of log analysis.īefore we get started, keep in mind that this tutorial uses Moogsoft - an AIOps platform - to analyze logs. We’ll also learn how AIOps can be applied to minimize the burden of sorting logs to find important data. In this article, we’ll demonstrate the capabilities of AIOps for managing IT operations by walking you through a tutorial for using AIOps to analyze logs from a demo environment. … big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination. The technological research and consulting firm Gartner that first introduced AIOps describes it as follows:
It enables enterprises to consolidate information - such as logs, metrics, events, and alerts - from various sources and leverage big data and machine learning techniques to unlock deeper insights. That’s why we use a special branch of AI called artificial intelligence for IT operations (AIOps) to reveal the deeper structure of copious data.ĪIOps sits at the intersection of big data and machine learning to improve the efficiency of IT operations. Digital data is everywhere, and its sheer volume and ambiguity often make it challenging for us humans to analyze.
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