  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.
