What is AIOps?
AIOps is used to automate and enhance IT operations using big data and machine learning. It’s the combination of artificial intelligence algorithms and human intervention to provide a full picture of the state and performance of the IT systems. In short, it is the future in digital transformation of the current IT Operations.
Evolution of AIOps
These days, the digital market space has overcome the traditional market and as it’s bound to grow, the digital data available to process is going to be simply gigantic. Taking inputs from the large data available from clouds systems, third party systems, IOT devices, SaaS integrations, mobile systems is no longer possible using plain automation or using manual intervention. As an IT engineer, retaining and processing these data becomes tedious after a certain limit, even with the help of automation. We need “intelligent systems” to cater to the ever-changing business requirements with top speed. This is where AIOps emerge.
Figure 1: AIOps platform:
How does AIOps work?
AIops uses the existing sources to gather information – like logs, monitoring systems, tickets, application alerts and performance test results from various channels. The mathematical model analyses these information to filter out noise and actual issue, which would have ordinarily taken huge manual hours to filter. Another algorithm cluster the related events to identify the root cause and our system from previous experience, will charter the solutions. Before deploying solutions, however, various teams are notified and with the help of human intervention, the solution is deployed. The continuous learning algorithm learns from this “experience” and stores it for future use. As the algorithms eventually evolve to take better shape, issues can be resolved at very short notice.
Figure 2: AIOps cycle:
Why switch to AIOps?
According to Gartner, 40% of the companies will be switching to AIOps by 2020. Looking at the key factors to make this switch:
- The volume of the incoming data – it takes more than a simple automation algorithm to sift through the large amounts of data being logged or monitored in today’s digital era and get a realistic picture of the exact issue. Deploying big data techniques hugely helps in this regard.
- Analyzing these large data in a shorter time and in an in-depth manner to produce a better picture on the performance bottlenecks is a vital part. Hidden patterns emerge once we employ ML algorithms to process these data.
- To spot issues in real time and react to it – not only for collecting large amounts of data from various platforms, but to spot the real time issues and react to the same at top speed, be it infrastructural or data related.
- When developers are switching to new technologies, IT Ops should do too, to be in tandem.
From big companies like Alibaba, already moving to AIOps from DevOps, we can easily say that the future is here.