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.

本文介绍了机器学习在现实世界应用中的重要性，探讨了 MLOps（机器学习运营）的兴起及其在解决模型部署和性能监控等挑战方面的重要性。通过回顾 MLOps 的演变和其与传统软件开发方法的关系，论文提出了将该系统与机器学习相结合以解决现有 MLOps 面临的问题并提高生产力的方法。本文重点关注自动化模型训练的重要性，以及通过版本控制系统确保训练过程的透明性和可重复性的方法。此外，还讨论了将机器学习组件集成到传统 CI/CD 流水线中面临的挑战，并提出了版本环境和容器化等解决方案。最后，论文强调了模型部署后持续监控和反馈循环的重要性，以保持模型的性能和可靠性。本文运用 Netflix 的案例研究和最佳实践，提出了成功实施 MLOps 实践的关键策略和经验教训，并为其他组织构建和优化自己的 MLOps 实践提供了有价值的参考。