MLOps -- Definitions, Tools and Challenges
It addresses the operationalization of machine learning systems for practitioners, but is incremental as it synthesizes existing knowledge without introducing new methods or data.
The paper provides an overview of Machine Learning Operations (MLOps), defining its components, tools, and challenges, and proposes a connection with AutoML to enhance system functionality.
This paper is an overview of the Machine Learning Operations (MLOps) area. Our aim is to define the operation and the components of such systems by highlighting the current problems and trends. In this context, we present the different tools and their usefulness in order to provide the corresponding guidelines. Moreover, the connection between MLOps and AutoML (Automated Machine Learning) is identified and how this combination could work is proposed.