AN INTELLIGENT DEVOPS PLATFORM RESEARCH AND DESIGN BASED ON MACHINE LEARNING

Authors

  • Dhaya Sindhu Battina Sr. Data Engineer & Department of Information Technology USA

Keywords:

DevOps, intelligent DevOps, Machine learning

Abstract

The main purpose of this paper is to review DevOps theoretical framework using the machine learning method. There have been several changes in the intelligent communication and Internet sectors as a result of the continual deepening and extension of IT business based on artificial intelligence, machine learning, and blockchain technologies [1]. Mature IT companies are required to deal with enormous volumes of data as part of their regular DevOps (Development & Operations) tasks. Over time, it became apparent that this data came from a variety of sources, was in a variety of formats, and had other difficulties [1]. DevOps developing computer software and hardware technologies that are both efficient and cost-effective has become a critical activity that must be completed. It's estimated that DevOps accounts for more than half of the SLC. In terms of overall business control, business risk management, and business cost management, it influences the whole IT company. Using machine learning methods to conduct research and design an intelligent DevOps platform, this project aims to increase the efficiency of DevOps engineers while also ensuring that DevOps has a high intelligence level. This project also helps to move DevOps towards informatization by helping engineers analyze large amounts of various system alarms [1].

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Published

2019-03-31

Issue

Vol. 6 No. 3 (2019): IJIERT

Section

Articles

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