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How AIOps Drives Efficient DevOps and IT Operations Automation

How AIOps and ML Drive Efficient DevOps and IT Operations Automation Blog Banner Image

Artificial Intelligence (AI) is a loaded term. While “Strong AI” – technology that can simulate and even surpass human brain activity – is still a thing of Sci-Fi, machine learning is a real-world application of AI, a way to train machines to learn from specific sets of data. One example of ML in the wild is the Nest Thermostat. For the first few weeks after installation, users must manually regulate the temperature as desired at different times of the day. Once the Nest has a reference dataset, it automatically adjusts the temperature based on your preferences.

While Strong AI can cause some unease, machine learning has been implemented with great success. In fact, Gartner coined the term “AIOps” to describe a handful of new use cases that leverage these advances in data science to increase operational efficiency. Let’s take a look at a few practical applications:

Cut through infrastructure and application noise with AIOps

The most comprehensive AIOps strategy starts with monitoring tools doing what they do best – detecting issues before they take down an entire service.

Predict and prevent outages

Most companies have infrastructure and application monitoring solutions that alert teams to issues when they occur. By adopting machine learning, monitoring systems become proactive – surfacing potential issues and triggering alerts before they strike.

Reduce alert fatigue

Another way ML can make life easier for IT Operations is by grouping alerts into episodes. This goes way beyond noise reduction – it looks at the context in each alert and makes inferences about related alerts before grouping them into one episode, speeding up incident resolution while also reducing alert fatigue for incident responders.

Dynamic thresholding

What if your system could account for and adapt to regular patterns in business activity and data? This makes it much easier for teams to respond to real problems as they happen.

Anomaly (outlier) detection

By pinpointing deviations from past behaviors or groups of behaviors, monitoring solutions can identify unusual events that might otherwise go unnoticed.

ML Incident Management Guide

Leverage ML to surface context and streamline collaboration

What happens when your monitoring solution pinpoints an issue that’s likely to impact your infrastructure in thirty minutes? If you don’t have a tool that directs those alerts to the right people at the right time, you’ve squandered your head-wind. Advanced incident response solutions bake ML into their offering to provide difficult-to-spot insights and easy collaboration for on-call teams.

Identify similar incidents

Even with appended annotations and metrics, it can be time-consuming to troubleshoot the best way to solve an incident. Now, once an alert makes it to the correct on-call expert, ML surfaces similar past incidents to show first responders exactly how past incidents were resolved. This context helps teams quickly identify potential solutions to even the most complex problems.

Suggest responders

When help is required, even after reviewing appended annotations, Suggested Responders can remove feelings of isolation by guiding first responders directly to the help they need when they need it.

As we can see, machine learning and AIOps encompass an array of different functionality but can make monitoring and incident response much more predictable. By shaving minutes (or hours) off of detection and response, companies can ultimately save millions of dollars in prevented downtime while also making on-call a more humane experience for all involved.


Learn how Splunk + VictorOps can help you leverage machine learning and artificial intelligence for highly effective AIOps. Or, try it yourself in a free, 14-day trial.

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