Enhancing DevOps Using Intelligent Techniques: Application of Artificial Intelligence and Machine Learning Techniques to DevOps

Sahana P. Shankar (M.S. Ramaiah University of Applied Sciences, India), Deepak Varadam (M.S. Ramaiah University of Applied Sciences, India), Aryan Bharadwaj (M.S. Ramaiah University of Applied Sciences, India), Shraddha Dayananda (M.S. Ramaiah University of Applied Sciences, India), Sarthak Agrawal (M.S. Ramaiah University of Applied Sciences, India), Ayush Jha (M.S. Ramaiah University of Applied Sciences, India), and Surya Tejas V. (M.S. Ramaiah University of Applied Sciences, India)
Copyright: © 2023 |Pages: 274
EISBN13: 9798369309872|DOI: 10.4018/978-1-6684-5859-4.ch012
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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.
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