Enhancing cloud based software engineering with machine learning
Khadka, Birendra (2024)
2024
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024053119014
https://urn.fi/URN:NBN:fi:amk-2024053119014
Tiivistelmä
With the ability to access virtualized resources on demand, cloud computing has developed into a game-changing technology that enables companies to adapt to changing market conditions and grow their infrastructure through dynamic expansion. In machine learning (ML), where computational resources might be very variable and in demand, this method is particularly helpful.
The main goal of this thesis is to streamline cloud-based software engineering processes by incorporating machine learning techniques. Machine learning algorithms are mostly used in cloud-based software development and deployment pipelines to automate tasks, improve decision-making, and optimize resource usage. The goal is to increase the effectiveness, stability, and scalability of cloud-based software systems by utilizing predictive analytics and data-driven insights.
Among the real-world examples presented in the research to show how machine learning may be applied to enhance speed, security, resource allocation, and service rollout are AWS, Netflix, Uber, and other well-known businesses. For cloud-based software engineering decision-making processes, these case studies provide useful insights.
The results of our tests show the potential benefits of integrating machine learning with intelligent decision-making and automation in a variety of domains. The results add to the growing body of knowledge on machine learning applications in software development and offer insightful information to both academics and industry practitioners.
The main goal of this thesis is to streamline cloud-based software engineering processes by incorporating machine learning techniques. Machine learning algorithms are mostly used in cloud-based software development and deployment pipelines to automate tasks, improve decision-making, and optimize resource usage. The goal is to increase the effectiveness, stability, and scalability of cloud-based software systems by utilizing predictive analytics and data-driven insights.
Among the real-world examples presented in the research to show how machine learning may be applied to enhance speed, security, resource allocation, and service rollout are AWS, Netflix, Uber, and other well-known businesses. For cloud-based software engineering decision-making processes, these case studies provide useful insights.
The results of our tests show the potential benefits of integrating machine learning with intelligent decision-making and automation in a variety of domains. The results add to the growing body of knowledge on machine learning applications in software development and offer insightful information to both academics and industry practitioners.