Machine learning has been able to bring a drastic change to how we look at things in many operations in the IT industry. Incident management is one such process that has been able to benefit off this concept immensely. Below are three main ways for “how Machine learning has affected incident management”
The first out of the three is that the reduction remediation durations. No longer will you need your best brains at work to spend too much time on these issues, and you can make sure your engineers are working on something far more productive at that time. Now, you can use a reliable automated approach to solve this issue. Doing so will take a lot of stress and pressure out of your employees because these situations demand a great deal of mental strength. Also, the remedies which are undertaken to make sure the applications are functioning as expected would be better and quicker. Hence the benefits are quick fixes, much productivity at work, and also minimizing the frustrations employees have to work with.
When you use an automated approach that is driven by Machine Learning at incident management you can go to the root cause. In the traditional methods, you attempt to get alerts of similar failures rather than addressing the root cause. When you fix it once and for all, the probabilities are higher for you to avoid the issues arising from the same cause. Getting rid of such issues in the future is always better than trying to evolve the alerts. I am not denying that alerts aren’t important though. But when you use machine learning, you can find the root cause and it is solved automatically without much hassle.
Last but not the least, when you are leveraging a machine-learning-based approach for incident management, the chances are very high for it to detect silent bugs. Silent bugs are those issues which are not have been the reason for any issue yet. In the traditional approaches, new releases are going through multiple stages of testing before the deployment and production to detect the bugs which could be irritating in the future. But as of now, the development life cycles have gained steam, and a major part of the testing takes place during the production only. Machine learning is capable of detecting the defects you never dreamed of. It will find silent bugs before them causing havoc in your system.
Last word is, it should be noted that these three results are changing as per the situations.
Also Read: Machine learning for banking
