Enhancing DevOps Using Intelligent Techniques

Author:

Shankar Sahana P.1ORCID,Varadam Deepak1,Bharadwaj Aryan1,Dayananda Shraddha1,Agrawal Sarthak1,Jha Ayush1,V. Surya Tejas1

Affiliation:

1. M.S. Ramaiah University of Applied Sciences, India

Abstract

Change is an inevitable part of any business. Customer satisfaction and building good will is the primary goal. The real success lies in the above two factors rather than money. Different businesses operate in different ways. Each one focuses on a different set of criteria and thus follows a different set of models. There are various models in the software development life cycle, such as the waterfall model, spiral model, V-model, and so on. These models have advantages and disadvantages and aid in the improvement of a company's workforce. They overcome the disadvantages of the previous model with each model. DevOps is the most recent model that is widely used. This chapter deals with DevOps, including the need, working, and how it differs from other models. This also looks into how intelligent techniques can be used to enhance the DevOps process for better productivity in the businesses (i.e., AIOps). It summarizes the different phases in DevOps, the corresponding machine learning or artificial algorithms that can be applied in the phases.

Publisher

IGI Global

Reference50 articles.

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