2022/02/07 by Satvik Garg, Pradyumn Pundir, Garg, Satvik +9 · 1 citation
Business, Management and Accounting · Computer Science · #Big Data and Business Intelligence #Cloud Computing and Resource Management #FOS: Computer and information sciences #Machine Learning (cs.LG) #Software Engineering (cs.SE) #Software System Performance and Reliability
paper · pdf · doi:10.48550/arxiv.2202.03541
openalex publication_date 2022/02/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Model deployment in machine learning has emerged as an intriguing field of research in recent years. It is comparable to the procedure defined for conventional software development. Continuous Integration and Continuous Delivery (CI/CD) have been shown to smooth down software advancement and speed up businesses when used in conjunction with development and operations (DevOps). Using CI/CD pipelines in an application that includes Machine Learning Operations (MLOps) components, on the other hand, has difficult difficulties, and pioneers in the area solve them by using unique tools, which is typically provided by cloud providers. This research provides a more in-depth look at the machine learning lifecycle and the key distinctions between DevOps and MLOps. In the MLOps approach, we discuss tools and approaches for executing the CI/CD pipeline of machine learning frameworks. Following that, we take a deep look into push and pull-based deployments in Github Operations (GitOps). Open exploration issues are also identified and added, which may guide future study.