What is AIOps? A Beginner's Guide

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Introduction to AIOps

AIOps, like DevOps before it, is a growing technological framework that’s bringing great change to a range of industries. Centred on the application of machine learning (ML) and big data science to IT operations problems, impacting a range of technologies and fields – especially those that do a lot of their work in the cloud. Various users – DevOps teams, infrastructure experts, and digitally transformed companies – enjoy the perks of utilizing the technology across a range of AIOps use cases. The implementation is still evolving, but AIOps is rapidly developing in line with modern technology.

What is AIOPS in Layman's Terms?

Here is a straightforward definition of AIOps from Gartner:

“AIOps is the application of machine learning (ML) and data science to IT operations’ problems.”

AIOps technologies bring together big data and ML tools to support a range of IT operations’ functions. These functions can include, but are not limited to: 

  • Availability and performance monitoring;
  • Event correlation and analysis:
  • IT service management and automation. 

An AIOps platform can ingest and analyze massive amounts of data, and through ML and forms of statistical inference, produce useful insights and/or interventions.

Applied to the world of applications, AIOps solutions facilitate the rapid and automated scanning of performance patterns; the detection of anomalies in time-series event data; and the pinpointing of the root cause of application performance issues.

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What makes an AIOps platform?

All systems share certain features:

Machine Learning and AI
The core feature of Artificial Intelligence for IT Operations Systems, machine learning (ML) uses predictive and intelligent analysis to supplement and enhance a system’s decision-making ability.

Real-Time Processing
AIOps systems need to be able to analyze and process large amounts of data at speed. Real-time processing allows enterprise IT organizations to respond immediately to issues like anomalies and security breaches.

Deep Reinforcement Learning

The best AIOps systems leverage deep reinforcement learning (DRL), which converts observed patterns and learned responses into ever more refined algorithmic behavior. With DRL, algorithmic output is used as a new or additional input to alter existing input values.

Pattern Recognition
A true AIOps system is able to recognize and follow complex rules and patterns, in order to accurately detect and assess events, and respond appropriately.

Domain Algorithms
Domain algorithms define the precise operations and decision-making processes that the AI will prioritize. These are specific to an IT organization’s goals and data in a certain industry or environment.

This is one of the key reasons why AIOps is receiving such enthusiasm from the industry. Effective AIOps solutions and systems reduce IT operators’ workloads by automating menial or repetitive tasks, increasing efficiency on the human side of the enterprise. 

Data Aggregation

Many Artificial Intelligence for IT Operations platforms carry out the collection and statistical synthesis of varying types of data from an eclectic range of sources.

What Happens Inside an AIOps System?

Inside any AIOps system, data from varying sources gets processed by a number of layers of machine learning algorithms. The output from those algorithms can then either be presented as insightful data at the end point, or be used as new input in a continuous cycle until the desired output is achieved.

A good example of AIOps use cases is the cloud optimization of applications, and some AIOps solutions that deal with this use case follow this flow:

After the app code has passed through the CI/CD pipeline, the AIOps-based Cloud Optimization tool begins to measure the performance of that code. 

The AIOps tool formulates predictions about which set of configurations can further improve the performance of the application, or reduce the cost incurred, or both.

Then, the AIOps tool tweaks the settings and configuration parameters, implements the changes, and runs tests. 

While this is going on, the AIOps tool measures data from the testing process and analyzes the data to learn how the changes affected the performance and/or cost. 

The AIOps tool then takes these learnings, compares them to previous data, and makes another set of predictions, which lead to a new set of configurations. 

This cycle, as implied by its name, runs repeatedly, non-stop. This means the system can keep on finding new ways to achieve the highest possible performance with the lowest possible cost.

The Rewards of AIOps

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So why AIOps? Why are so many companies rushing to implement various AIOps tools? The benefits to a business are numerous:

Speed. AIOps tools operate with unbeatable speed and agility. Because of their real-time processing capabilities, AIOps solutions can offer the insights that a company’s IT operators need with little to no delay. 

Automation. By automating redundant processes and activity, AIOps can free up more time (and mental capacity) for IT operators to focus on more relevant concerns, like solving issues and developing systems.

Holistic view. AIOps tools can provide your business with an all-encompassing view into your IT environment, allowing you to see data from everywhere: compute, network, storage; physical, virtual, and cloud.

Increased business responsiveness. AIOps solutions can capture useful information and make it available in context, allowing your company to make data-driven decisions and refine their response to various scenarios.

Performance and cost optimization. An AIOps platform can also help your company optimize your application’s performance and bring down costs incurred. Considering only 43% of companies across the industry are confident of their application’s performance, this is much-needed.

Increased reliability and decreased downtime. Optimized applications means fewer issues to fix, less friction between specialists and service providers, and minimal disruption to end users.

AIOps Use Cases

AIOps is mostly used by companies with complex operations relying on cloud IT operations. These companies face lots of issues around the complexity and scale of their environments. The industries in question can vary widely, but the common denominators that link companies using AIOps are:

  1. Their large scale;
  2. Their rapid growth or change, and;
  3. They’re need for business and IT agility.

AIOps users can vary by role and department. They include: 

  • DevOps teams. Companies who’ve adopted (or are adopting) a DevOps model face a struggle juggling the different roles involved. AIOps can integrate with DevOps systems and bring new efficiencies. AIOps tools give a holistic, bird’s-eye view of systems. This can be a big help in ensuring increased agility and responsiveness, leading to project success.
  • Cloud or hybrid infrastructure users. Moving to the cloud presents a problem that people often only see as a solution: it’s a much larger environment. Though you can do infinitely more within the cloud, it is also easy to get lost in it. On-prem servers had baked in limits. In the cloud it’s easy to overprovision, and then struggle to control costs. But AIOps solutions can help companies make sense of the functions and the changes that happen in the cloud. An AIOps tool can even help a company optimize application performance and save them millions of dollars in cost.
  • Digitally-transforming companies. Many companies are rushing to implement various forms of digital transformation. In this landscape, there is a growing demand for AIOps systems that can help ease the transition into the digital sphere. AIOps solutions can help IT operations get on par with the speed required to operate in a digitally-transformed company and deliver the kind of support that the organization requires.

Some companies are enjoying the perks of AIOps right now. A major FinTech leader, for example, managed to shave 61% off their costs with the help of our handy AIOps tool. The same tool has also helped Ancestry, the global leader in family history and consumer genomics, achieve an average of 50% reduction in their operation costs, with no performance degradation. And it’s not just these: a smattering of other AIOps use cases can be found in journals and technology articles online.

The Future Is Here

Blog - Why the Rise of DevOps

“Our Age of Anxiety is, in great part, the result of trying to do today’s job with yesterday’s tools and yesterday’s concepts.”

Marshall McLuhan

The IT industry is forever evolving. DevOps has largely replaced the traditional IT department. Microservices have replaced the monolithic model. The cloud has partly replaced on-prem, and so on.

What AIOps is, is just another turn of this wheel. With the revolutionary emergence of AI and machine learning, it is a framework aimed at maximizing the potential of the current IT landscape, so that technologies can flourish. The scale of cloud computing has changed everything. To avoid falling behind, users need systems designed for this new normal. AIOps is one of those systems.

The transition to Artificial Intelligence for IT Operations is still in its early phase, but the battle is heating up and there are already success stories. VCs are placing bets, and vendors small and large are bringing new solutions to the market. These solutions solve a plethora of problems, and still, more AIOps use cases are popping up everyday. It is only logical, then, that AIOps must evolve over the coming years and continue to enable DevOps to embrace the scale and speed of modern development.

On average, when they implement our AIOps Cloud Optimization tool, Opsani users experience a 2.5x increase in efficiency, or a 40-70% decrease in cost. Get in touch today to see for yourself.