What is AIOps – a Next-Gen DevOps Perspective?

What is AIOps

Introduction

Firms’ digital technology has led to the creation of an IT system that includes large data stores and hybrid and multi-cloud systems. Huge data sets have been created due to the growth of various channels, customer contacts, and device platforms. Firms find it hard to manage IT, which is essential to improving efficiency, monitoring, and pursuing business continuity. Due to the flaws of the current IT solutions for managing data, businesses are utilising AIOps for various tasks. These include predicting and analysing consumer behaviour, seeing deviations and figuring out why they occur, and giving prescriptive counsel. Finding connections that are causing problems in an IT network is helpful.

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What is AIOps?

It is software that utilises artificial intelligence, machine learning, and big data to enhance the usability of all core IT activities. Automation, IT help desk, reporting tools, activity tracking, and analytics, among other things, are examples of possible IT services. To boost the efficiency and output of the DevOps framework, AIOps uses data science and machine learning.

The advantages of integrating AI into the value chain are:

  • Processing all data types quickly and precisely from various sources. As a result, data integrity is protected, and real facts are obtained.
  • Analysing huge data sets to raw data that DevOps engineers can use to assess and modify systems (if needed).
  • We are recognising patterns in events and setting automatic actions to act.

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AIOps vs DevOps: the difference

The best way to deploy high-quality software solutions in any organisation is by using DevOps, which may be the best software method. Why, then, has AIOps become such an essential tool?

  • The main contrast between AIOps and DevOps is the former’s multi-layered structure, which may fully manage IT operations and enable statistical analysis. DevOps change, on the other hand, involves utilising agile processes and using them to automate self-service operations.
  • Tasks are carried out in real time by AIOps without the aid of humans. Typical DevOps cannot know, much less carry out, the IT tasks it can assess and manage to the data sources.
  • AIOps is capable of a wide range of data-driven analytics tasks, including managing log data, historical data, and streaming data. By using display skills, it can help organisations with diverse business divisions to access data.
  • Although DevOps quality assurance can use containers and ai tools to manage the deployment of the build, it falls short in other areas like security and regulation and system operations.
  • Through CI/CD pipelines, DevOps QA aids in reducing the SDLC, while AIOps provides a global system to handle and manage IT tasks with huge amounts of data.
  • As the next corporate applications running across different cloud platforms need to be tracked and managed in real-time, the role of AIOps will rise in the days ahead.

Why should companies use AIOps?

An AIOps method that uses AI and ML would be vital for creating and deploying next-generation business apps. Using this cutting-edge method has the following advantages:

Eliminates IT noise: 

IT noise can make it hard to detect faults, lead teams to false positives, and hide root-cause events. Also, it may result in poor performance, higher operating risks and costs, and the rejection of enterprise digital initiatives. By creating linked occurrences that point to the cause, AIOps-driven tools can decrease or even remove noise.

Superior customer service: 

AIOps may perform data modelling and make decisions relating to future events, with the value of the user experience driving profit as the most important aspect. AIOps can detect events that impact the availability and performance of IT systems by analysing data. Also, locating the source of IT problems can directly assist in their cure.

Better collaboration: 

AIOps can break down core business and simplify workflow for IT groups and other business units, resulting in better teamwork. For teams to easily understand their duties and take action on them, they can create custom dashboards and reports.

Enhance service delivery: 

By analysing usage trends, support tickets, and users, AI, ML, and automation can aid any firm’s delivery staff in solving issues. It can detect basic quality issues and assist in their repair by using probable cause analytics.

Conclusion

AIOps can be unique from DevOps test automation, which is the de facto norm for allowing the automation of IT processes. It can legally replace DevOps as its next-generation image by reducing firms’ reliance on particular automation tools. Also, AIOps can track the behaviour of IT networks, and combining data resources, may improve efficiency and boost profit. FITA Academy offers the best DevOps Training in Coimbatore to grow as a successful DevOps Developer.

ALSO READ: DevOps Interview Questions and Answers

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