3 Foundations of ITOps Autonomy

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Have you ever wondered about a future possibility where human IT Operators and Intelligent systems work alongside in a shared virtual room? Let us guess everyone has, right! Well, this question or imagination may seem like a science fiction movie where intelligent systems do most of the heavy lifting while humans supervise them. Also, we are about to enter an era called AIOps with the help of Artificial intelligence development companies.

The progress of AI-augmented teams may seem quite a way off, but AI/ML-based tools are taking their slow approach towards helping ITOps, DevOps, and SRE teams and making this assumption a near-future possibility. Moreover, all technical advancements are made to meet data processing or performance efficiency needs on a scale. In other words, the volume of data can make a considerable difference in digital operations.

Need For ITOps Autonomy:

If we consider an Artificial intelligence development company, we can also find similar trends in which digital tools generate data at an increased rate alongside certain operational complexities. There can be several reasons and factors, such as cloud migration, merger and acquisition-related activities, and adoption of modern technologies and methodologies, like CI/CD, Clusters (microservices), and more.

Because of these new mergers ups and adoptions, sometimes the data generation rate reaches a great extent, making it complex for ITOps teams when things are often out of control. And from where the outage and service disruption problems begin or may become their everyday stress. Also, this may pull away IT teams from their strategy-based, mission-critical projects and find core reasons behind the existing issues.

Sometimes motion tools worsen the problem; meanwhile, scaling ITOps teams seem the only sound option in terms of cost-effectiveness and for the long run. There are also other solutions to prevent such disruption and outages, such as automation and its extended version known as Autonomy.

What is ITOps Autonomy?

Autonomy is an ITOps approach operating with minimal or without any human intervention. Autonomy often depends on automation of the risk management cycle where there is a need for human supervision, just as a single operator supervising modern and complex auto-driving car assembly processes.

So, what could be the right way to achieve ITOps Autonomy? That is where the pillars or foundations of ITOps come into the picture.

3 Pillars of ITOps Autonomy:
Automation:

Being an ITOps team, you must automate each step of the incident management cycle; of course, it will take some time and the help of AI/ML technologies. You can also consider this process as the autonomous driving analogy.

A few years back, there were only assumptions and forecasts related to self-driving cars or process automation. Still, today, we can say that we have achieved this Autonomy in some ideal conditions. Meanwhile, some cautious or enthusiastic IT people will continue to achieve higher levels of Autonomy, where the automation and intelligent decision-making skills of application would be possible for most conditions or at an absolute level.

For example, there is a software application which experiences some alerts and automatically raises a ticket in the system of the assigned IT person. It suggests the current progress of AI-based ITOps Autonomy. Still, in the later stages, that application can learn from the previously occurred similar events and automatically fix the problem intelligently. However, the authority of passing that suggested fix would be in the hands of that assigned IT person.

Prediction and Prevention:

In most cases, at the moment of incident ticket allocation, the responder constantly notes the incident with its priority. Also, they rely on their past experiences and own logic while deciding on an appropriate response or remedy.

But when this prioritizing process comes to humans’ hands for the IT environment, it is vast and complex, which is more like “out of human ability.” Therefore, there is a need to leverage the capabilities of Artificial intelligence development company in the prediction and prevention process.

AI-based incident management system identifies patterns by analyzing a haystack of data and linking incidents with similar root causes. For instance, this system not only resolves these incidents quickly but also predicts their potential outcomes. This prediction can help the ITOps team implement preventative actions manually before outages or by writing some automation codes. Over time, this process will undoubtedly be completely automated, which may not require supervision or human intervention.

Democratization:

The Democratization of AI is the last pillar or foundation of ITOps to achieve its complete Autonomy. Making it easier for new users to turn it on in a few clicks to enjoy the benefits of AIOps. Rather than letting the organization have an army of data scientists as a backbone.

Democratization makes information available for everyone in an easily approachable, applicable, and actionable manner. In other words, AIOps & ITOps models should contain user-friendly interfaces (for example, GUI based), so users can access them without learning them thoroughly.

For example, AIOps and ITOps platforms must suggest to admins an effortless way to prioritize incidents for an Artificial intelligence development company having from entry to expert-level experience. To make these platforms more democratized, they need to implement more built-in features into them.

Wrapping Up:

The rate of data generation will continue to increase exponentially. Also to keep control over them and prevent IT systems from experiencing outages; Autonomy is the only way in ITOps. Being an AIOps or ITOps vendor, you must consider these three pillars of ITOps autonomy to achieve better customer experiences.

If you’re looking for the leading firm providing ITOps and AIOps solutions in India, consider CloudStakes Technology your best bet. Contact us to know more about our AI-based automated solutions and services.

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