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How Artificial Intelligence will Transform IT Operations and DevOps

To state that DevOps and IT operations teams will face new challenges in the coming years sounds a bit redundant, as their core responsibility is to solve problems and overcome challenges.

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However, with the dramatic pace in which the current landscape of processes, technologies, and tools are changing, it has become quite problematic to cope with it. Moreover, the pressure business users have been putting on DevOps and IT operations teams is staggering, demanding that everything should be solved with a tap on an app. However, at the backend, handling issues is a different ball game; the users can’t even imagine how difficult it is to find a problem and solve it.

One of the biggest challenges IT operations and DevOps teams face nowadays is being able to pinpoint the small yet potentially harmful issues in large streams of Big Data being logged in their environment. Put simply, it is just like finding a needle in the haystack.

If you work in the IT department of a company with online presence that boasts 24/7 availability, here is a scenario that may sound familiar to you. Assume that you get a call in the middle of the night from an angry customer or your boss complaining about a failed credit card transaction or an application crash. You go to your laptop right away and open the log management system. You see there are a more than a hundred thousand messages logged at the set timeframe – a data set impossible for a human being to review line by line.

So what do you do in such a situation?

It is the story of every IT operations and DevOps professional; they spend many sleepless nights, navigating through the sea of log entries to find critical events that triggered a specific event. This is where real-time and centralized log analytics come to the rescue. It helps them in understanding the essential aspects of their log data, and easily identify the main issues. With this, the troubleshooting process becomes a walk in the park, making it shorter and more effective, as well as enabling experts to predict the future problems.

AI and Its Effect on IT Operations and DevOps

While Artificial Intelligence (AI) used to be the buzzword a few decades ago, it is now being commonly applied across different industries for a diverse range of purposes. Combining big data, AI, and human domain knowledge, technologists and scientists have become able to create astounding breakthroughs and opportunities, which used to be possible in science fiction novels and movies only.

As IT operations become agile and dynamic, they are also getting immensely complex. The human mind is no longer capable of keeping up with the velocity, volume, and variety of Big Data streaming through daily operations, making AI a powerful and essential tool for optimizing the analyzing and decision-making processes. AI helps in filling the gaps between humans and Big Data, giving them the required operational intelligence and speed to significantly waive off the burden of troubleshooting and real-time decision-making.

Addressing the Elephant in the Room – How AI can Help

In all the above situations, one thing is common; these companies need a solution – as discussed in the beginning – that helps IT and DevOps teams to quickly find problems in the mountain of log data entries. To identify that single log entry putting cracks in the environment and crashing your applications, wouldn’t it be easy if you just knew what kind of error you are looking for to filter your log data? Of course, it would cut down the amount of work by half.

One solution can be to have a platform that has collected data from the internet about all kinds of related incidents, observed how people using similar setups resolved them in their systems, and scanned through your system to identify the potential problems. One way to achieve this is to design a system that mimics how a user investigates, monitors, and troubleshoots events, and allows it to develop an understating how humans interact with data instead of trying to analyze the data itself. For example, this technology can be similar to Amazon’s product recommendation system and Google’s PageRank algorithm, but it will be focused on log data.

Introducing Cognitive Insights

A recent technology implements a solution as envisioned by this post. The technology – which has been generating quite a lot of buzz lately- is called Cognitive Insights. This groundbreaking technology uses machine-learning algorithms to match human domain knowledge with log data, along with open source repositories, discussion forums, and social thread. Using all this information, it makes a data reservoir of relevant insights that may contain solutions to a wide range of critical issues, faced by IT operations and DevOps teams on a daily basis.

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