4 min read

Woah, woah, woah! Wait a minute! First there was that game SpecOps that I usually sucked at, then there came ITOps and DevOps that took the world by storm, now there’s another something-Ops?? Well, believe it or not, there is, and they’re calling it AIOps.

What does AIOps stand for?

AIOps basically means Artificial Intelligence for IT Operations. It means IT operations are enhanced by using analytics and machine learning to analyze the data that’s collected from various IT operations tools and devices. This helps in spotting and reacting to issues in real time.

AIOps (Artificial Intelligence for IT Operations)

Coined by Gartner, the term has grown in popularity over the past year. Gartner believes that AIOps will be a major transformation for ITOps professionals mainly due to the fact that traditional IT operations cannot cope with the modern digital transformation.

Why is AIOps important?

With the massive and rapid shift towards cloud adoption, automation and continuous improvement, AIOps is here to take care of the new entrants into the digital ecosystem – Machine agents, artificial intelligence, IoT devices, etc. These new entrants are impossible to service and maintain by humans and with billions of devices connected together, the only way forward is to employ algorithms that tackle known problems. Some of the solutions it provides are maintaining high availability and monitoring performance, event correlation and analysis, automation and IT service management.

How does AIOps work?

As depicted in Gartner’s diagram, there are two primary components to AIOps.

  1. Big Data
  2. Machine Learning

Data is gathered from the enterprise. You then implement a comprehensive analytics and machine learning strategy alongside the combined IT data (monitoring data + job logs + tickets + incident logs). The processed data yields continuous insights, continuous improvements and fixes.

Data sources

It bridges three different IT disciplines to accomplish its goals:

  1. Service management
  2. Performance management, and
  3. Automation

To put it simply, it is a strategic focus. It argues for a new approach in a world where big data and machine learning have changed everything.

How to move from ITOps to AIOps

Machine Learning

Most of AIOps will involve supervised learning and professionals will need a good understanding of the underlying algorithms. Now don’t get me wrong, they don’t need to be full blown data scientists to build the system, but just having sufficient knowledge to be able to train the system to pick up anomalies. Auditing these systems to ensure they’re performing the tasks as per the initial vision is necessary and this will go hand in hand with scripting them.

Understanding modern application technologies

With the rise of Agile software development and other modern methodologies, AIOps professionals are expected to know all about microservices, APIs, CI/CD, containers, etc. With the giant leaps that cloud development is taking, it is expected to gain visibility into cloud deployments, with an emphasis on cost and performance.

Security

Security is critical, for example, it’s important for personnel to understand how to engage a denial of service attack or maybe a ransomware attack, like the ones we’ve seen in the recent past. Training machines to detect/predict such events is pertinent to AIOps.

The key tools in AIOps

There are a wide variety of AIOps platforms available in the market that bring AI and Intelligence to IT Operations. One of the most noteworthy ones is Splunk, which has recently incorporated AI for intelligence driven operations. Another one is the Moogsoft AIOps platform, that is quite similar to Splunk. BMC also has entered the fray, launching TrueSight 11, their AIOps platform that promises to address use cases to improve performance and capacity management, the service desk, and application development disciplines. Gartner has a handy list of top platforms. If you’re planning the transition from ITOps, do check out the list. Companies like Frankfurt Cargo Services and Revtrak have already added the AI to their Ops.

So, are you going to make the transition? According to Gartner, 40% of large enterprises would have made the transition to AIOps by 2022. If you’re one of them, I recommend you do it for the right reasons, but don’t do it overnight. The transition needs to be gradual and well planned. The first thing you need to do is getting your enterprise data together. If you don’t have sufficient data that’s worthy of analysis, AIOps isn’t going to help you much.

Read more: Bridging the gap between data science and DevOps with DataOps.


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