While many companies aim to use Machine Learning (ML) models, transitioning to deployment and practical application of such models can be very time-consuming and technically challenging. To address this, MLOps (ML Operations) offers processes, tools, practices, and patterns to bring ML models into operation. A large number of tools and platforms have been created to support architects and developers in creating practical solutions. However, specific needs vary strongly in a situation-dependent manner, and a good overview of their characteristics is missing, making the architect’s task very challenging.
We conducted a systematic literature review (SLR) to identify key features, patterns, and platform characteristics. As a result, we provide an overview of the technical design space of MLOps and insights into different ML platforms. Our review can help architects select MLOps components and support them in their development efforts.