Investigate the challenges and opportunities of MLOps

Abstract: MLOps is becoming a widespread practice in modern machine learning and data science. The word ”MLOps” combines machine learning technology and business operation process. Many business companies are applying machine learning techniques to improve their business targets and increase their profits. However, machine learning tasks involve complex applications and many technical stakeholders to perform a single system. It is not easy for non-technical operation staff to understand and proceed with the data science and machine learning processes without the aid of technical stakeholders. The communication effort between different stakeholders costs highly since the entire machine learning and operation processes are redundant and complicated. Therefore, MLOps provides insight for businesses to simplify the system workflow. MLOps pipeline automation simplified the processes in data, model, and production perspectives. This project researches several kinds of literature to identify the process of MLOps, including the phases and processes of MLOps, the necessary components for pipeline automation, and the concept of continuous integration and continuous delivery with the aid of MLOps. Also, two methods were selected to introduce a general MLOps pipeline design and implementation and the software tools and technologies selected in each MLOps process. Finally, a comparison section was provided to evaluate different software tools and technologies in five standard criteria.