  This article introduces the importance of machine learning in real-world
applications and explores the rise of MLOps (Machine Learning Operations) and
its importance for solving challenges such as model deployment and performance
monitoring. By reviewing the evolution of MLOps and its relationship to
traditional software development methods, the paper proposes ways to integrate
the system into machine learning to solve the problems faced by existing MLOps
and improve productivity. This paper focuses on the importance of automated
model training, and the method to ensure the transparency and repeatability of
the training process through version control system. In addition, the
challenges of integrating machine learning components into traditional CI/CD
pipelines are discussed, and solutions such as versioning environments and
containerization are proposed. Finally, the paper emphasizes the importance of
continuous monitoring and feedback loops after model deployment to maintain
model performance and reliability. Using case studies and best practices from
Netflix, the article presents key strategies and lessons learned for successful
implementation of MLOps practices, providing valuable references for other
organizations to build and optimize their own MLOps practices.
