What is AIOps and what is its structure
What is AIOps and what is its structure - infoweb24.com

What is AIOps and what is its structure?

AIOps is the application of artificial intelligence in information technology operations. This is important for monitoring and managing modern hybrid, dynamic, distributed technology components and essential components. AIOps helps IT and DevOps teams work smarter and faster by algorithmically analyzing IT data; Thus, groups can identify digital services issues more quickly and resolve them quickly; Of course, before business operations and customers are affected.

Using AIOps, technology teams can control the complexity and vast amount of data generated by their modern IT environments, thus avoiding downtime, maintaining downtime, and providing ongoing services. With IT at the heart of digital transformation efforts, AIOps allows organizations to operate at the speed a modern business needs. In the following, we will examine this topic further.

AIOps is an artificial intelligence platform for today and the future

You can not manage the dynamic and ever-changing IT environment with outdated tools. The evolution of information technology infrastructures (transitions from fixed and predictable physical systems to software-defined resources that change and configure rapidly) requires the same technology and processes to manage them. The complexity of managing the operations of modern IT environments can be seen at three levels:

Systems of AIOps

In essence, the complexity of systems is important when they are modular, distributed, and dynamic, and their components will be transient.


The second layer is the data that these systems generate about their internal operations. Reports, criteria, effects, event logs, and more are at this level. This data is complex due to its large size, variety of features, and redundancy.

Tools of AIOps

The third outer layer is the complexity of the tools used to monitor and manage data and systems. There are increasingly limited-performance tools that do not always work together, thus creating operational and data silos.

As IT infrastructures evolve, older systems fall away because they rely on a predetermined static representation of a largely homogeneous, independent IT environment.

AIOps uses machine learning and data science to keep IT teams informed in real-time of any issues – including new and unforeseen problems whose rules have not yet been developed – that affect the availability and performance of digital services.

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How does AIOps work?

What is AIOps and what is its structure
What is AIOps and what is its structure – infoweb24.com

Not all AIOps products are created equal. To get the most value, the organization must deploy it as an independent platform that receives data from all IT monitoring sources and acts as a central system of interaction. Such a platform should use five types of algorithms that make the five main dimensions of IT operations monitoring fully automated and simple:

1- Selecting data of AIOps

Catching large amounts of highly redundant and noisy IT data generated by a modern IT environment and selecting data elements indicates that there is a problem and often means filtering up to 99% of this data.

2- Discovering the pattern

Relate and find relationships between selected and meaningful data elements and group them for further analysis.

3- Inference

Identify the root causes of recurring problems and issues so that you can act on what has been discovered.

4- Cooperation of AIOps

Informing appropriate operators and teams and facilitating collaboration between them, especially when individuals are geographically dispersed, as well as maintaining data on events that can speed up future detection of similar problems.

5- Automation

Automate response and correction as much as possible to make solutions more accurate and faster.

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The result of AIOps speech

Overall, AIOps is used by organizations around the world in all types of activities, industries of different sizes, and for a variety of scenarios. These businesses include companies with complex and large environments, small and medium-sized companies native, DevOps teams in various organizations, organizations with hybrid cloud space, and high-content environments alongside digitally evolving businesses.

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