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Model deployment in machine learning has emerged as an intriguing field ofresearch in recent years. It is comparable to the procedure defined forconventional software development. Continuous Integration and ContinuousDelivery (CI/CD) have been shown to smooth down software advancement and speedup businesses when used in conjunction with development and operations(DevOps). Using CI/CD pipelines in an application that includes MachineLearning Operations (MLOps) components, on the other hand, has difficultdifficulties, and pioneers in the area solve them by using unique tools, whichis typically provided by cloud providers. This research provides a morein-depth look at the machine learning lifecycle and the key distinctionsbetween DevOps and MLOps. In the MLOps approach, we discuss tools andapproaches for executing the CI/CD pipeline of machine learning frameworks.Following that, we take a deep look into push and pull-based deployments inGithub Operations (GitOps). Open exploration issues are also identified andadded, which may guide future study.

On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps