• Dhaya Sindhu Battina

This research paper explored how machine learning can be leveraged in improving CI/CD Pipeline and DevOps Processes. As a result of the intrinsic complexity of software creation and maintenance - not just in terms of technical complexity, but also from a human standpoint - some obstacles may be handled as learning problems. Software processes and products may benefit from machine learning approaches by gaining insight into tactics that can lead to better quality [1]. An ongoing study area predicts how likely something will be to fail due to a defect. Companies may increase their company value by operating in an agile mode, according to DevOps rules, which allows for more rapid communication, decision-making, and problem-solving [1]. It is described in this article how La Redoute's IT department is doing continuing research into the use of machine learning methods to increase the performance of tools and methodologies inside the DevOps pipeline.

DevOps, CI, CD, machine learning, CI/CD Pipeline, software development

"Improving La Redoute’s CI/CD Pipeline and DevOps Processes by Applying Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 10, page no.f224-f227, October-2021, Available :http://www.jetir.org/papers/JETIR2110532.pdf

"Improving La Redoute’s CI/CD Pipeline and DevOps Processes by Applying Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 10, page no. ppf224-f227, October-2021, Available at : http://www.jetir.org/papers/JETIR2110532.pdf