How Can Artificial Intelligence Improve DevOps

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How_Can-Artificial-Intelligence-Improve-DevOps How Can Artificial Intelligence Improve DevOpsToday, there are some problem areas faces by DevOps which are largely spin around data, i.e accessing the massive amount of data, taking actions on it, managing alerts and so on. Besides, there are errors also caused by human interference. Though, Artificial Intelligence works heavily with data and can assist improve DevOps.

Some of the problem areas face by DevOps today includes Human errors- It is difficult to reform and fix the errors when tests and deployments are done by manually. In some cases, software development is development is outsourced in companies; in those cases, there is lack of coordination between the dev and ops teams. Change management- several businesses have change management processes well in place, but they are outmoded for DevOps. Monitoring- it assures smooth functioning in agile.  Though, numerous companies don’t have the experience in monitoring the pipeline and infrastructure. Further, only monitoring the infrastructure is not enough, there also requires monitoring of application performance, solutions need to be logged and analytics need to be tracked. So, here are some ways how can AI improve DevOps. The major critical issue faced by DevOps teams is the lack of uncontrolled access to data. There is also a large pool of data, while the teams seldom view all of the data and focus on the outliers. The outliers only run as an indicator but do not provide robust insights. So AI can assemble and organize data from compound sources for repeated usage. Structured data is much easier to access and comprehend that volume of raw data will assist in predictive analysis and eventually a better decision-making process.

In DevOps, complete automation is also a critical issue, because of many tasks in this is routine and require human intervention. So, here an AI model can automate these repeatable tasks and accelerate the process significantly. A well-trained model enhances the scope of complexity of the tasks which can be automated by machines. It can assist to obtain the least human interference so that developers can concentrate on more complicated interactive queries. Hence, through complete automation, errors can be reproduced and fixed quickly.