With increasing efficiency and sophistication, the IT environment is becoming extremely complex too. The recent shift to microservices and containers has further added to the already large number of components that go into a single application, which means the challenge is equally big when it comes to orchestrating all of them.
The ability of IT Ops teams to handle such complexities is fairly limited and hiring more resources to configure, deploy, and manage them is not very cost-effective.
This is where Artificial Intelligence for IT Operations (AIOps) comes into play. None come close to AIOps when it comes to leveraging Big Data, data analytics, and machine learning to offer a high level of customization along with invaluable insights necessary to cater to modern infrastructure.
Here’s what you should know if you are contemplating moving towards AIOps.
As automated tools entered the scene, IT Ops teams realized that despite improved efficiency, these tools were incapable of making automated decisions based on data, and therefore required considerable manual effort even then.
AIOps presented a more refined way of integrating data analytics into IT Ops, supporting more scalable workflows aligned with organizational goals.
Anomaly detection — This is definitely the most basic one since you can trigger a remedial action only after detecting anomalies within data.
Causal analysis — Root cause analysis is required for issues to be resolved quickly and effectively. AIOps plays a pivotal role here.
Prediction — Automated predictions about the future can be made using AIOps powered tools. For instance, you can find out how and when user traffic can possibly change and then react to address it.
Alarm management — Intelligent remediation, closed-loop remediation, is kicked in without necessitating human intervention.
DevOps had brought about a cultural shift in organizations, and in that sense, AIOps is pretty similar in effect and impact. AIOps is helping enterprises discover holistic insights from connected and disparate data to bring about decision-automation to make them better and more agile.
It is important for enterprises to break free from traditional silos as data should be generated and used keeping the ‘observability’ aspect in mind for the entire company, not just one department.
Thanks to AIOps, typical IT Ops admins are now transitioning into the role of Site Reliability Engineers helping them utilize information more efficiently and tackle issues in a more effective manner.
While both AIOps and DevOps share the same goal of making organizations better and more productive, AIOps can make DevOps practices more effective by reducing the noise that gets in the way of productivity. For example, AIOps streamlines the alerts and notifications from various platforms so that it becomes easier for DevOps engineers to address them. It would be safe to assume that AIOps complements the goals of DevOps engineers and enterprises effortlessly.
No matter what the team size, organizations will always struggle with the most common issue of having too much to do in too little time.
Luckily, there’s a lot AIOps can do for you in this regard. From helping you create a machine learning model to processing data to make it flexible enough to accommodate new information, AIOps can be just the value add-on you need.
Those who have been using AIOps would know the role of a well-trained machine learning algorithm in attaining and maintaining the high quality of data. Also, ‘real-time’ is the buzz word here since most use cases require real-time data processing.
So for instance, if the use case in question is detecting anomalies, then it is important to get information quickly so that you can prevent a security breach. The same applies for all use cases where the rationale is to get to a problem and resolve it in the fastest possible manner.
