From itproportal.com
Most of a data scientists’ time is spent on non data science tasks, such as building or setting up ML infrastructure, DevOps semantics or many resource conflicts or dependencies. Ideally, this time should be spent on building algorithms, research, experiments, and monitoring/iterating models in production. MLOps discipline was developed as a form of DevOps for machine learning tasks, to help decrease the technical complexity and the technical debt associated with machine learning infrastructure.