AIOps is a term coined back in 2016 when Gartner published a report on how software systems with big data can use artificial intelligence and/or machine learning to enhance and/or partially replace a broad range of IT operations tasks and processes, including but not limited to availability, performance monitoring, event correlations, analysis, and automation.

With the evolution of technology, IT operations have become more complex than ever before. Highly scalable systems with a lot of horizontal and vertical compute spread globally, ML/AI, AR/VR/XR, IoT, data science, and analytics with data of exabytes and beyond have changed the way IT operations are managed in the last few years.

AIOps as a practice area was created to address the current need to manage IT operations. Analytics and machine learning are applied to big data, including application logs, system metrics, and everything in between. This enables us to find patterns, identify causes of problems and predict future impacts, helping the teams to automate and improve their accuracy and speed, enabling IT staff to be more effective in meeting demands.

As with every ML/AI process, data is the core of the AIOps solution. To achieve the required accuracy and lead time, both historical and real-time data generated by machines and humans are needed. Centralized data from a variety of sources will help algorithms identify better correlations, resulting in more curated outcomes.

There are four key use cases in the industry.

1. Anomaly detection: Machine learning is a powerful tool when we talk about identifying patterns, data outliers, and events that stand out from the historical data. These outliers are called anomalies. The best part is that, with continuous learning, machine learning algorithms can identify outliers even if there were none in the historical data. With AIOps tools like InsightFinder, it becomes faster to detect anomalies, which is vital in complex systems to prevent failures that often happen but are not immediately clear to the IT teams.
2. Performance and capacity analysis: With the increasing size of IT systems, it is not humanly possible to analyze the amount of data that exists today to identify performance issues. Again, AIOps tools like InsightFinder and others can address the need to analyze the increasing complexity of data and size, accurately identifying the service levels or performance, and predicting the need to add or remove capacity to IT systems. This is applicable to every company in any industry using IT systems.
3. Correlation and analysis: AIOps is a powerful tool to bring a variety of data together from multiple IT systems, creating a correlation between events and clubbing them together to avoid a flood of warnings. This approach reduces the burden of IT teams by reducing unnecessary event traffic, so they can focus on key events to identify the cause and take appropriate actions. This is most critical in real-time systems where the impact is huge, like financial and banking institutions.
4. Automation: AIOps can help IT organizations cobble information together from multiple systems in a meaningful way and automate actions that an IT operations team would otherwise take manually. Taking a simple example of a disk running out of space due to varying volume needs throughout the week. Instead of a team member logging into the system and freeing up space by deleting unnecessary files and logs, AIOps can help you automate the scale in and out by predicting the change in demand.

In conclusion, AIOps’ primary benefit is to predict and prevent incidents before they happen. By reducing the task of bringing data together from multiple sources, detecting the anomalies, and generating correlation for easy root cause analysis, AIOps helps save a lot of teams’ time and has a significant impact in improving MTTR, MTTI, and MTTD.

Let’s switch gears from reactive to proactive Ops.