  Machine Learning (ML) has become a fast-growing, trending approach in
solution development in practice. Deep Learning (DL) which is a subset of ML,
learns using deep neural networks to simulate the human brain. It trains
machines to learn techniques and processes individually using computer
algorithms, which is also considered to be a role of Artificial Intelligence
(AI). In this paper, we study current technical issues related to software
development and delivery in organizations that work on ML projects. Therefore,
the importance of the Machine Learning Operations (MLOps) concept, which can
deliver appropriate solutions for such concerns, is discussed. We investigate
commercially available MLOps tool support in software development. The
comparison between MLOps tools analyzes the performance of each system and its
use cases. Moreover, we examine the features and usability of MLOps tools to
identify the most appropriate tool support for given scenarios. Finally, we
recognize that there is a shortage in the availability of a fully functional
MLOps platform on which processes can be automated by reducing human
intervention.
