AIOps — IT needs your prompt help.

Original article was published on Artificial Intelligence on Medium

AIOps to the rescue!

It is obvious that ITOps teams need more and more help, especially in contexts where remote working comes in strongly and becomes the option chosen by employees, generating complexity when managing all these incidents. Companies desire to continue meeting their customer demands by continuously implementing changes, but this requires great agility.

How does AIOps work?

Artificial intelligence for IT operations, also known as AIOps or IT operations analytics (ITOA), is the integration of AI and automation tools into IT operation processes. Basically we refer to the implementation of software that applies AI / ML techniques for the early identification of event correlation, anomaly detection and causality determination errors. In this way, we are able to generate real-time business performance KPIs, allowing IT teams to resolve incidents faster, while reducing their number.

Gartner defines AIOps as a combination of Big Data and machine learning functionalities used to automate IT Ops processes, including event correlation, anomaly detection and causality determination.

AIOps acts as a facilitator for IT agility through proactive and autonomous operations.

It consists of three main steps: Observe — Engage — Act
and these steps are taken in a continuous cycle.

Source: Gartner

Observe → Performance Analysis
In this first task, the software will focus on real-time processing of data sources, as well as monitoring of traditional processes and log events, to automatically detect anomalies in the data and cluster / classify them according to their similarity.

Engage → Experience Management
Once classified, the system will notify the IT team of these anomalies and will warn about possible bottlenecks or possible performance problems of the different applications.

Act → Delivery Automation
As the system keeps learning from these anomalies, it will become able to find a solution without even relying on human intervention, allowing problems to be solved before they even reach end users or IT teams can become aware of them.

Why is AIOps trending now?

With the increase in the practice of Machine Learning and AI, its algorithms are increasingly able to perform tasks with fewer errors, faster, less expensive and at scale, that were previously done manually.

Today, there is too much data for humans to manage it functionally or manually and that’s where AI and ML come into play.

Here are the main reasons for the AIOps trend:

  • More performance data to analyze, according to Splunk, AIOps vendor, it states that 73% of the data remains untapped by IT teams.
  • Shorter response time expectations, as companies and B2C applications become more responsive.
  • More complex structures, with a greater sophistication of IT architectures, becoming an ever greater challenge.
  • Dynamic environments, any change requires a new adjustment.