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
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