AI and ML Transforming DevOps

AI and ML Transforming DevOps

The rise of AI and ML has increased the efficiency of the DevOps team. AI tools are more robust and faster than traditional DevOps tools. AI and ML help DevOps perform better from the starting point to deliver the software to the customers. They reduce the need for human touch and increase the quality of the software.

AI is promising to transform DevOps positively. As a result, it enhances the quality of the teams as well as the software. AI uses Machine Learning algorithms to monitor and improve the process continuously. Listed below are four ways of transformation:

1.      Automated Code Review & Analysis

AI and ML use smart tools for code management. AI analyzes vast volumes of the data set. ML learns the patterns and their changes to tell what to do next. After analyzing previous records, ML tools can also understand what the developers are doing and why they change code. The collaboration of AI and ML in DevOps results in early detection of bugs, higher security, faster code reviews, traffic reduction, etc.

2.      Auto Test

Every software needs functional and non-functional testing at every stage. Developers can use AI and ML-based self-healing test code. It makes the maintaining process easier. Instead of days, it takes few minutes to complete the process. It also becomes more stable and reliable.

3.      AIOps

AI and ML are leading the DevOps to AIOps. AIOps provides smart solutions like APM (Application Performance Monitoring) and leverage ITIM (IT Infrastructure Monitoring) and ITSM (IT Service Monitoring). It helps to gain control over the application and the visibility of data. With AIOps, AI and ML perform better in observing the trends and forecasting.

4.      Zero code

AI and ML can create autotests by analyzing app flows, screens, and elements in the software building environment. Generally, robust test codes are unavailable and expensive. Zero test code allows team members to participate in test automation. Developers can also use it to create innovative features.

AI and ML optimize error in the DevOps process and make it robust and effective. They reduce the time of reviewing the code. DevOps team can deliver the software to the customers in the shortest possible time. AI and ML work as a solution to face business challenges.