Join this group to discuss the various aspects of Machine Learning Systems, operational challenges, and technological directions. All contributions are welcome.
A nice definition of MLOps is presented in 2023 overview paper by Kreuzberg et al which I reproduce below:
"MLOps (Machine Learning Operations) is a paradigm, including aspects like best practices, sets of concepts, as well as a development culture when it comes to the end-to- end conceptualization, implementation, monitoring, deployment, and scalability of machine learning products. Most of all, it is an engineering practice that leverages three contributing disciplines: machine learning, software engineering (especially DevOps), and data engineering. MLOps is aimed at productionizing machine learning systems by bridging the gap between development (Dev) and operations (Ops). Essentially, MLOps aims to facilitate the creation of machine learning products by leveraging these principles: CI/CD automation, workflow orchestration, reproducibility; versioning of data, model, and code; collaboration; con- tinuous ML training and evaluation; ML metadata track- ing and logging; continuous monitoring; and feedback loops."
