How AI/ML can Automate DevOps

AI/ML integration in DevOps can be transformative for software development. It can enable faster code development, improve the efficiency and reliability of the software, and automate the analysis of massive amounts of data.

- Phani Kiran

The concept of DevOps was born with the purpose of fostering collaboration between the development and operations teams in order to streamline the software development life cycle. Numerous organisations are already reaping the benefits of DevOps. And now, AI and ML are being increasingly used in DevOps pipelines. The aim is to increase the operational efficiency and productivity. A shift towards automation can improve the overall performance of the system. In order to get optimum results, engineering teams adopt AI and ML techniques that can make maximum use of the underlying data and improve the overall DevOps experience.

Steps to include AI/ML in DevOps pipelines

By integrating AI/ML in DevOps, massive amounts of data generated in the DevOps pipeline can be analysed. Different AI/ML algorithms that suit the needs of the organisation can be applied. Here are the key steps:

Define the goal to use AI/ML in DevOps

Identify the relevant tools and technologies 

Leverage cloud providers’ APIs if available

Analyse DevOps data and gather appropriate training and testing data

Correlate different sets of inputs to find the best course of action

Build and finalise the AI/ML models