DevOps helps enterprises to build software at a fast pace and with minimal issues. The time to market is accelerated and the bugs are fixed faster in continuous deployment with the help of automated tools. AI is much in line with DevOps as the main focus is on automating the process and with AI the system can identify patterns, anticipate issues and provide solutions. The proactive approach improves the overall efficiency of the software development life cycle. So let’s have a look at how AI is transforming DevOps.
Feedback Loop and Correlate Data
The main role of DevOps is to take continuous feedback at every stage of the process. often people use performance monitoring tools to get feedback on running applications. These tools gather much information in the form of log files, data sheets, performance matrix, and other types. The monitoring tools use machine learning to identify the issues early and make suggestions. The DevOps teams use these suggestions to make the necessary improvements to the application. Many times teams use two or more tools to monitor the health of the app and the data from all the platforms can be correlated by the help of machine learning to get a more deep understanding of the app functioning.
AI is changing DevOps for good by enhancing the software development process and making testing more efficient. Whether it is regression testing, user acceptance testing or functional testing, these all produce a large amount of data. AI can figure out patterns in the data collected in the form of results and identify poor coding practices which produce a lot of errors. This information can be used by the DevOps teams to increase their efficiency.
DevSecOps is one of the essential aspects of software development as security is the key to any successful software implementation. Distribution denial of service attacks are increasing and the business needs to prepare themselves to protect their security systems from hackers. DevSecOps can be augmented using artificial intelligence to enhance security by central logging architecture to record threats and running machine learning based anomaly detection. This will help businesses proactively attenuate the attack from hackers and DDOS.
DevOps approach might create scenarios where the team receive an overwhelming amount of alerts without any priority tag. This will create ruckus in the teams as it will be very difficult to handle all the alerts in the continuous development environment. AI can help in this scenario by tagging the alerts and prioritizing them so that the urgent ones can be worked upon immediately.
Root Cause Analysis
To fix an issue permanently, a root cause analysis is necessary. Although it might take time to do it compared to fixing the issue with a patch which will provide the instant solution. In order to find the root cause of an issue, the developers will have to spend time which will delay the release of the product. AI can speed up the process by finding patterns in the data collected and implement to fix the root cause.
The collected data can be used by implementing AI to find a pattern and speeding up the development process. The organized data is more useful and makes prediction possible. The best practice is to use machine learning to automate the tasks which are time-consuming which will ensure the smooth and effective functioning of the DevOps teams.