MLOps: A Review
This is an incremental review paper addressing the need for better MLOps tools to reduce human involvement in ML projects.
This paper reviews Machine Learning Operations (MLOps) methods to help create user-friendly software and select appropriate tool structures for projects, analyzing 22 papers and noting the scarcity of fully effective self-regulating MLOps methods.
Recently, Machine Learning (ML) has become a widely accepted method for significant progress that is rapidly evolving. Since it employs computational methods to teach machines and produce acceptable answers. The significance of the Machine Learning Operations (MLOps) methods, which can provide acceptable answers for such problems, is examined in this study. To assist in the creation of software that is simple to use, the authors research MLOps methods. To choose the best tool structure for certain projects, the authors also assess the features and operability of various MLOps methods. A total of 22 papers were assessed that attempted to apply the MLOps idea. Finally, the authors admit the scarcity of fully effective MLOps methods based on which advancements can self-regulate by limiting human engagement.