Optimizing Continuous Integration and Delivery in DevOps with Automated Machine Learning Pipelines
Keywords:
Continuous Integration, Continuous DeliveryAbstract
Continuous Integration and Delivery (CI/CD) have become critical aspects of modern software development, enabling rapid deployment and continuous improvement of applications. The integration of Automated Machine Learning (AutoML) pipelines into DevOps is emerging as a powerful strategy to enhance CI/CD processes. This paper explores how AutoML can optimize the CI/CD workflow by automating the machine learning model training, validation, and deployment phases. We examine the ways in which AutoML pipelines can streamline model production, improve model accuracy, and reduce the time-to-market for AI-driven applications. Additionally, the paper analyzes challenges in scaling AutoML within DevOps environments and provides strategies to overcome these hurdles for seamless integration.
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